Self-Calibrating BCIs: Ranking and Recovery of Mental Targets Without Labels
Authors: Jonathan Grizou
Carlos De la Torre-Ortiz
Tuukka Ruotsalo
Venue: NeurIPS 2025 poster ()
Keywords: EEG
brain-computer interfacing
calibration-free
Primary area: neuroscience_and_cognitive_science

Abstract: We consider the problem of recovering a mental target (e.g., an image of a face) that a participant has in mind from paired EEG (i.e., brain responses) and image (i.e., perceived faces) data collected during interactive sessions without access to labeled information. The problem has been previously explored with labeled data but not via self-calibration, where labeled data is unavailable. Here, we present the first framework and an algorithm, CURSOR, that learns to recover unknown mental targets without access to labeled data or pre-trained decoders. Our experiments on naturalistic images of faces demonstrate that CURSOR can (1) predict image similarity scores that correlate with human perceptual judgments without any label information, (2) use these scores to rank stimuli against an unknown mental target, and (3) generate new stimuli indistinguishable from the unknown mental target (validated via a user study, N=53). We release the brain response data set (N=29), associated face images used as stimuli data, and a codebase to initiate further research on this novel task.

Self-Calibrating BCIs: Ranking and Recovery of Mental Targets Without Labels
Jonathan Grizou" GrizAI
Carlos de Torre-Ortiz- University of Helsinki
University of Glasgow
carlos . het..eeellooorrrrtiiieeeeeliiiii
jonathan wrw.aaririioourui...omm
Tuukka Ruotsalo
LUT University
University of Copenhagen
tuukka. www.sallluuu...........
Abstract
We consider the problem of recovering a mental target (e.g., an image of a face) that participant has in mind from paired EEG (i.e., brain responses) and image (i.e., perceived faces) data collected during interactive sessions without access labeled information. The problem has been previously explored with labeled data but not via self-calibration, where labeled data unavailable. Here, we present the first framework and an algorithm, CURSOR, that learns to recover unknown mental targets without access to labeled data or pre- pre-trained trained decoders. Our experiments naturalistic images of faces demonstrate that CURSOR can (1) predict image similarity scores that correlate with human perceptual judgments without any label information, (2) these scores rank stimuli against unknown mental target, and (3) generate new stimuli indistinguishable from the unknown mental target (validated via user study, N 53). We release the brain response data set (N = 29), associated face images used as stimuli data, and a codebase to initiate further research on this novel task.
Website
Code and Data http://gtttttt.cooggrrrr/////epe set.-eeseeeecllllbbbriinnnnoaooo
Introduction
We tackle the problem of recovering a mental target (c.g., an image of a face) that participant holds in mind, using only unlabeled EEG responses (i.c., brain activity) recorded while the participant views a sequence of unlabeled images (i.e., perceived faces). These images, along with the mental target, are represented high-dimensional vectors in the latent embedding space of generative model. Thus, unlike traditional brain-computer interfaces (BCIs) that classify brain responses into a few discrete task requires mapping EEG responses distances
classes (e.g., yes/no up//down/leftrright), in this high-dimensional space in order to recover the mental target iteratively. The challenge lies in probing these continuous nigh-dimensional spaces (image and EEG spaces) without discretizing them into labeled classes and only relying on paired data of image and brain representations.
Previous BCI methods operating in continuous domains such decoding distance mental target - required an explicit calibration phase relying on labeled data (i.c., known targets) to train a
"qual contribution
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

R= 00.01
Z
0.9
(
fig
R -0.8
0.001
1
hg
R 1211
Figure I: (Left) Participants are instructed to hold a mental target x", represented as z², unknown to our system, while sequence of i = (1....,A) NN images shown to them and their EEG responses are being recorded. CURSOR'S goal is to recover 2" with this information alone and fully unsupervised. (Middle) CURSOR generates hypothetical target images, e.g. h1, and builds dedicated estimation problem to predict the distance di between hy and z; from the corresponding EEG response e,, for all i. (Right) The performance of estimator on this task is the score (hh)) )
attached h1- The hypothesis with the highest score h arg max S(h) is assumed to be the unknown mental target. Images are generated from 2, E R552 embeddings with EEG responses e, E 2203, We collected N = 92334 {2z, e pairs for this study from 29 participants.
In contrast, this paper introduces method that requires no prior supervision or labeled examples, which we term fully self-calibrating. Self-Calibrating BCIs (SC-BCIs). in general, are those that can infer the decoding relationship without an experimenter-prowe peroviiddd labeled training set 23). Prior SC-BCI research, however, has primarily focused on tasks involving discrete stimulus set and categorical feedback (e.g.. P300 spellers, where the target is one option from a small known set, and feedback is match/non-match) [38 25 32 1 70). The challenge of self-calibrating in continuous domains is significantly greater, requiring a search over theoretically infinite latent stimulus space using a continuously-valuee feedback decoded from brain signals (e.g., a latent-space classification). To our
decoder (7I).
distance or other regressed value, in contrast to a discrete match/non- knowledge, this paper presents the first SC-BCI framework designed for continuous domains. Our method, CURSOR, achieves label-frec self-calibration and iteratively estimates the unknown mental target, thus implicitly recovering the necessary information (i.e., labels) for subsequent decoder training. We demonstrate the effectiveness of CURSOR in a mental target recovery task.
To enable our study, we collected large dataset (N = 9234) of EEG responses (I2203) recorded as participants (N 29) viewed face images generated from a latent embedding space (R:512 while holding a mental target image in mind. Collecting novel data was necessary because existing datasets [19 26) focus on categorization tasks with participants passively observing stimuli. Com- pared to existing datasets, ours is, our knowledge, the only existing dataset suitable for this task because (1) stimuli are represented within a continuous space, (2) participants actively engage with a target in mind, (3) quantitative distance metrics between stimuli can be defined, and (4) ground-truth data for human distance judgment between two stimuli are obtained through a user study.
Equipped with a novel suitable dataset, we propose CURSOR (Consistency- based Unsupervised Regression for Siiilaiity-bsser Optimization and Ranking), the first self celf-calibg BCI algorithm for continuous domains. As illustrated in Figure I CURSOR first generates hypothetical mental target as an embedding vector representing a face a user might have in mind. Given this hypothesis, CURSOR constructs hypothesis-specific dataset that transforms observed faces into distances to this hypothetic target (Alg. [T] line 2). CURSOR then trains an estimator to decode EEG responses into these hypothetical distances. The performance of this estimator, relative to that of one trained deliberately misaligned dataset, defines the CURSOR score for the hypothesis under consideration (Alg. line 8). The CURSOR score should peak when evaluated at the (unknown) true mental target, because the EEG responses were recorded in that context and, therefore, should provide the strongest basis for predicting the corresponding distances.
CURSOR does not require access to any ground-truth supervision labels and thus solves the SC-BCI challenge. Our experiments confirm that CURSOR can identify the mental target user has in mind from unlabeled EEG responses without requiring calibration, pre-trained decoders, heuristies, or labels. To our knowledge, this is the first work extending SC-BCIs to continuous domains.

---

Our contributions are as follows:
1. We introduce the first SC-BCI framework and associated dataset for continuous domains.
2. We propose CURSOR, the first SC-BCI algorithm for continuous domains, supported by empirical validation across ranking and optimization tasks, allowing generating the mental image target and recovering ground truth labels.
3. We empirically analyze CURSOR'S 's performance under various conditions, including estima- tor type, data representation, and sensitivity to the amount of data.
Related Work
BCI-driven visual decoding tasks. We build the established distinction between passive decoding (reconstructing viewed stimuli) and the active, iterative search of a mental image target (36).
In passive EEG-guided generative decoding, EEG is recorded while participant views unseen images, and each signal projected into a semantic latent space (e.g.. CLIP or diffusion) from which a representative image is synthesized [4] 27 The training uses time-aligned EEG-image pairs without category labels and can scale well with self-supervised objectives. Nevertheless, these methods typically generate an image from the target class rather than the specific stimulus instance (c.g., some face, not the exact face identity). Some works introduced fixed, sorck-strcttured stimuli resulting in temporal confounds that have been shown to inflate accuracy (2) 45 7]. Our approach avoids these confounds and does not belong to the passive decoding family.
For iterative mental target search, the system updates its estimate of the mental target based on preceding EEG responses, progressively steering the stimulus sequence toward single mental target (e.g., a specific face image). Previous work relied on labeled target trials to learn the EEG- driven update rule and adapt the target estimate with heuristics based on binary labels (36] or distance estimates (71) decoded from EEG. Our method preserves the iterative search behavior of the latter while eliminating the label requirement, recovering a concrete target within continuous high-dimensional generative manifold without pre-training data ground-trutt labels at any stage.
Although our work adheres to the iterative search framework, we do not report results from a fully closed-loop task [32 60). Instead, our evaluation is conducted offline (iterative but non- anniintracttiv) using EEG recorded from a pre-generated stimulus sequence. All iterative analyses are performed post-hoc. Moreover, we do not employ active sampling; EEG- pairs are drawn randomly without replacement from the offline dataset.
SC-BCIs in classification contexts. Self-calibrated BCIs (SC-BCIs) 31 23 have been primarily developed for classification tasks. For instance, (38) relied on Sopectation-Maximizattee (EM), assuming signals can be linearly projected onto two one-dimensional Gaussian distributions (yes/no responses) and aiming to maximize the distance between these distributions. Later designs used classification accuracy on datasets attached to each possible target as scoring metric [25] 32).
which (67] 70) approximated using the distances between the mean two classes. Label proportion (LP),, initially proposed by (59), was applied to BCIs by (30). I.P assumes known, unbalanced ratio of labels in stimuli-response pairs that can be exploited to label clusters identified from unsupervised
clustering methods. Further work has extended IP to reinforcement learning (22 from unlabeled
reward to break symmetry after applying contrastive learning, and [74] explored the combination of
EM and LP methods.
Self-calibration was also explored in
Interaction (4 Human-Computer Interfaces 61
23 81), Security and Privacy [47] and Language Games 776 5] always in classification context.
3 Self-calibration Problem Formulation
We consider interaction sessions as pairs of stimuli and responses, where stimuli are images of faces I E X, represented as embedding vectors E from a known generative model G: Z + X. We rely on the same model to collect paired (x, z) for simplicity, but other methods could be used to generate and embed stimuli, respectively, which could be part of future research. User responses are EEG signals e E, recorded post-stimuli. An interaction session sequence of N stimuli-response
3

---

pairs VN {(2, e:) During interactions, participants guide the system by mentally focusing on target æ². represented as 2n and unknown the algorithm, while they observe sequence of stimuli xa, represented as The aim is to estimate & that approximates =" using VN-
Similarity equivalence. Participants observe only without explicit knowledge of Z, but we know EEG responses e, encode perceptual similarities X, correlating with distances d, Z (72). In other words. EEG responses e do not encode x" directly but "how far" 2 is from 2², This implies the existence of a function fa.- : E + F mapping EEGs e, to distances de Z, although it not directly accessible. Similarly, we assume the existence of a similarity function p=+ Z - R that
returns a distance d, between mental target =" and an observed stimulus 2,. Hence:
fe.- = d. = Pa- (2))
(I)
with =", Oz-, di P=- unknown but access to (ei, 2;) pairs.
Error estimation. According to Equation 1 the task reduces to identifying 2 that minimizes the error between two estimates fe, (e+) and (2));,, based observed (2,, ex) pairs in y... This can be measured on the entire dataset by defining an error- trrordbased score such as:
2 = arg min (RROR((f_(e)) (z()))
(2)
1=1.NN
where ERROR() is metric function defined to best suit the problem.
The SC-BCI challenge lies in the reciprocal dependency between 0₂ and Ps. Both are apriori unknown and need to be learned. But learn Qz, we need to access {ei, d;} pairs, which can only be acquired by knowing P= to reconstruct de based on 2;; and vice versa, to learn Pz. we need 0..
4 CURSOR Algorithm
CURSOR, our proposed algorithm, solves this SC-BCI challenge by (1) defining similarity function o= for any z, which allows us to break the dependency deadlock in Equation P (2) defining an
ERROR function function that controls for the variability between datasets generated via P2: and (3) searching the hypothesis space of mental targets Z guided by the ERROR scores.
In the following, use Z as hypothetic mental target to be evaluated in Equation 2 We use to differentiate hypotheses from observations 2 clearly, ground truth mental target x", and best estimate 2, which all exist in Z.
(1) Defining similarity function. We define ph (2.) ||h zu 24,22, which assume to be valid for any hypothesis target h and any observed stimuli 24. This choice reflects the assumption that the perceptual similarity between observed image and target image é, as encoded in their EEG responses, correlates linearly with Euclidean distances in the structured latent space Z, in agreement with the findings of [72). Knowing Ph allows the estimation of Equation 2 by breaking the reciprocal dependency between f% and Pa for a given h. We use Pk (22) to build first estimate of d,, which in turn can be used estimate 0j via (c,, d:) pairs.
(2) Defining a ERROR function. Given Ph (2.). we construct a dataset r associated where dh = Ph (2:) (Alg. I line 2p. With this dataset, we can estimate 0n by training an estimator on - Equipped with both fo., and Ph. we can rely well established error metrics pz()), where lower values indicate stronger predictability. for Equation 2. such as RMSE(ffe pe),
{(e, di B
However, comparing RMSE scores directly is unsuitable because the distribution of d values in ry will vary between hypothesis h. even post-standardzzation. For example, a hypothesis hy "far away" from all observed 2, would lead a narrow distribution of d which is highly predictable. Hence, an RMSE score based on iy could compare favorably even with respect to the ground truth dataset r
To standardize errors across datasets, we compute a relative gain against a baseline score computed from misaligned stimuli- remuulirespooee pairs of the same dataset. In practice, a permutation produces

---

shuffled dataset I (Alg.
line 5
The final CURSOR score S(h) is defined by the ratio of the
respective RMSE scores (Alg
line &
as:
S(h) = RMSE (eo) pa())
[ (co) Px(=)l2
(c)
So, (e) (
with S relative error-ratio function which is to be maximized so h arg max (hh).
Although we instantiate Eq. 3 with RMSE, other standard choices, such as negative log-likeling bog-likelihood are in principle equally viable. In our setup, estimators are primarily point predictors and squared- training aligns naturally with RMSE, which we adopt for simplicity and consistency. Crucially, our relative gain (h) is designed to neutralize distributional differences across h.
Algorithm 1 CURSOR: Scoring Function
Require: Stimuli-response
1: function S(h)
dataset v {(22. }
2: Construct hypothesis dataset r {(e,, )
Require: Target hypothesis
3: Train estimator So. on 1
h E Z
4:
Compute RMSE, = RMSE( (c),
Require: Similarity function
5:
Generate shuffled dataset I = {(Co(s) )
p=(=>; = |/h zill2
Require: Estimator fo -> TR.
6:
Train shuffled estimator for.. on r N.o Compute shuffled RMSE,, = RMSE( (ca), return RMSE,, aRRMSR
Require:
Error function
7:
RMSE(8. v²) VN 11 - 8016 112
8:
(3) Searching the hypothesis space. CURSOR scores can be used in downstream tasks such as ranking and optimization. Indeed, hypotheses do not need to be among observed stimuli or within fixed test set, allowing any stimulus representation in Z to be scored, thus allowing S' to guide an optimization process throughout the latent space.
Neurophysiological Data Acquisition
To experiments, dataset of artificial human face images and associated EEG responses was collected from 31 participants, with 2 dropped due to data corruption.
Generating stimuli for the experiment. A pretrained GAN 20 the CelebA-HQ dataset (37 was used to generate stimuli images. The GAN maps its latent space Z in R512 to the image space X as G + X. We sampled the generator using 512-dimensional Gaussian noise to produce images and selected 17 target images without visual artifacts, represented by 2 latent vectors. For each target, stimuli were generated by sampling along a diagonal trajectory in the 512-dimensional latent space, moving equally far in every coordinate direction by adding a small, monotonically increasing positive value to each component of the source image latent representation. This approach is an established best practice in face research to ensure small, smooth, and monotonic changes in facial appearance between stimuli
62 35 22]. The sampling itself employed logarithmic spacing (denser near 0). This choice is motivated by its utility in BCI and psychometric paradigms (78, [8)) as efficiently focuses data acquisition near the target, where the resulting changes in brain activity are expected to be most sensitive to small stimulus variations
[73
[4 13]
Although the standard logarithmic sampling strategy ensures data quality, we recognize introduces an acquisition bias that limits direct expansion to online closed-loop settings. We stress-tested for this potential bias by: (i) evaluating the ranking task using uniformly sampled hypotheses, which are decoupled from the big-bd acquisition; and (ii) conducting ablation studies where near-target stimuli were systematically removed (Figure and Appendix C.I
BCI task, EEG processing, and dataset. Data consist of EEG responses elicited while subjects viewed images at varying similarity distances to a cued target in an RSVP paradigm. Before the RSVP stream, subjects were shown the target and instructed to make a mental note of it. Images then
Preittrained model and code:

---

appeared every 500 ms, aiming to evoke a P3-like response. EEG was recorded using 32-channel EasyCap system (29 effective channels after removing corrupted ones) and signals were processed offline using MNF for Python [2]] Standard filtering, time-locking into "EEG epochs," and artifact rejection were applied. Temporal BEG data was windowed into 7 equidistant time windows within the 50-800 ms range after stimulus onset. Within each of the 7 windows, samples are averaged across time for each channel (after baseline correction), p one scalar per (channel, window); stacking across 29 channels and 7 windows results in i2 vector. Our final dataset consists of 9234 stimuli-response pairs, with each entry composed of a z vector in IR 12512, stimuli 2; vector in R512, and EEG response vector e; in IR 203 The complete details can be found in Appendix A
6 Experiments
We evaluated CURSOR'S ability to rank and optimize facial stimuli against an unknown mental target based on unlabeled EEG responses. We describe our experimental framework, dataset preparation, and comparative studies, including ablations and control conditions. In addition, we validated our approach through human perception studies.
6.1 Datasets preparation for simulation runs
The original dataset comprise
pairs (stimulus 2;, EEG response e4) collected across multiple experimental sessions and different participants, meaning not all pairs were associated with the same target face. To create a sufficiently large, unified dataset for our simulation runs where every pair is associated with a single target face - we generated equivalent datasets. Given previous work confirming that the EEG response primarily encodes the Euclidean distance d(2,, 2²) in the latent Z-space (72), we could generate new stimuli 2/ that preserved the original statistical properties for any new target >new- Starting from a random 512-D initialization, we used second-orde -oderr numerical solver [50] 75] to minimize the distance- distance-matching objective and recover z at distance d, around "new. such that d( ) Ri d(zz; *original). Specifically, we created 10 dataset variants for each of the 17 target faces utilized in our neurophysiological experiments, yielding 170 stimuli-response datasets, each containing N=9234 pairs.
For ranking tasks, we built separate hypothesis sets of L 60 faces for each of the 17 target faces. Each hypothesis h; is generated at distance di from the target, with di sampled uniformly in [0, 46.16]. 46.16 being our dataset's 's maximum d value, ensuring our evaluation is within observed bounds.
6.2 Evaluation frameworks
Our study faces computational challenges due to dighesiimnsi data: stimuli (image embedding representation) in R212 and responses (flattened EEG representations) in 2020 making search a difficult process without a large sampling budget. Evaluating S is also computationally intensive, requiring vvoss-valiattion training of estimators on the 203D response space at each iteration. The need for multiple runs, baselines, and ablation studies compounds these challenges. We thus adopted two-stage evaluation approach: ranking predefined set of hypotheses and then optimizing in cimensioonally-rdduce spaces.
Ranking a predefined set of hypotheses. For ranking evaluations, we assume the system has access to finite set of L hypotheses H. {h1, ..,, ...., ...,, which allows us compute score on finite set of candidates. To simplify the interpretation of results, we included the mental target 2" in H. which guarantees the oracle baseline of the "similarity to top rank" metrics is 0 on Figure 3 right. This is by no means a requirement of our approach as we also demonstrate optimization results on dimensionally reduced spaces (see Figure 4). We rank the hypotheses directly by their CURSOR'S scores, breaking ties randomly. The following metrics are then evaluated:
Score-distance correlation. The Pearson's correlation coefficient R(S(h), P2- CURSOR scores and ground-truth target distances.
P2- (h)) between
Target rank. The rank of the target stimuli as measured by rank(=*) EvEN I(S(h) Z
S(²")) where I((-) is the indicator function.
Similarity at top rank. The similarity of the best ranked hypothesis to the ground-truth target Pa (h).

---

Optimizing in dimensionally-rrdueee spaces. To demonstrate the feasibility of optimizing target stimulus based on our scoring function S, we reduced the dimensions of e for faster score calculation and of z for faster convergence. We applied Principal Component Analysis (PCA) to reduce e to R220 and to RR10 with Appendix B detailiing our rationale with computational evidence. We conducted optimization experiments in these reduced spaces. Covariance Matrix Adaptation Evolution Strategy (28). implemented via Optuna (3) using default parameters, was chosen for optimization due to the high-ddmensiional, gradient-free nature of our problem. Optimization bounds were set to [-15, 15] per reduced dimension, corresponding to maximum distances in the 512D space that align with ranking experiments (47.42 vs 46.16), allowing fair comparison between ranking and optimization performance. We report the similarity of the optimized stimuli with the ground truth target P=- ( projected back to the original Z to allow a meaningful comparison with the ranking and human-experiment results.
6.3 Comparative and ablation studies
For comprehensive evaluation, we evaluated the impact of different estimator models, included two control conditions, ran multiple data scarcity scenarios, and tested with two EEG signal representa- tions. The computational environment detailed in Appendix C.3
Estimators. Our method requires training estimators fo, F compute the Self- CafibCalibrttion score (h)) We compared performance using Linear Regression (LR), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP) as implemented in the Scikit-learn library (56). The relative model simplicity allows for rigorous evaluation within our computational constraints, and linear models are known to perform well in EEG tasks (55 69 42 8 When training estimator parameters 0, both EEG and distances d are standardized by removing the mean and scaling to unit variance for each feature.
Each (h) estimate is the average of a 10-fold 90%/10% randomly partitioned cross-validation procedure. This cross- moos-validation is essential for two reasons: (1) it ensures the estimators do not train and test on the same data, and (2) it provides the held-out data necessary to compute the relative scores that define Sh)). We specifically address potential overfitting by computing S(h) as ratio of errors. Furthermore, for each cross- cross-validation fold, the aligned RMSE and the shuffled RMSE are computed on the identical held-out fold. Thus, if an estimator overfits performs poorly, the aligned and shuffled errors should be similar, resulting in (h) R 1, which is easily detected and can prevent premature decisions in online settings.
As detailed in Appendix C.I.
we decided not to pre-tune estimators' meta-paraaeters using ground- truth information because our approach is meant to be fully unsupervised, and doing so would not represent a realistic SC-BCI scenario. Nonetheless, we assessed the performance of SVR and MLP with offline hyperparameter optimization, which we detail in Appendix E.T
Control conditions. We benchmarked CURSOR using theoretically ineffective estimators for com- puting Sih). Our control conditions include: 1) a dummy estimator (Dummy) predicting d as the average of all distances in Thy without considering e, and 2) Linear Regressor that always Shuffle stimuli and response pairs prior to training (R- Both baselines should perform at chance level, confirming that our algorithm's 's information gain comes solely from EEG responses. For ranking, results are compared to random guessing. For L 60 candidates, the average target rank is 30, and the expected d of a top-ranked face 23.08.
Data and distance ablations. We evaluated our algorithm with varying dataset sizes (N) from 100 to 9234 to assess performance impact. Each dataset size was produced via uniform random sampling I' without replacement. We also tested sensitivity to near-target stimuli by removing stimuli-response pairs below distance thresholds from the target face (0 to 40), reducing dataset sizes from 9234 to 806. We ran parallel experiments with uniformly down-sampled datasets matching each threshold's 's size to control for dataset size effects. Further details can be found in Appendix C.I
Alternative EEG representations. To follow the representation learning with linear model evalua- tion framework [T], we re-ran most experiments using EEGNet embeddings of the raw EEG responses, which has shown ttthe-of-the-att performance in many tasks (4)). EEGPT 77 is
7

---

more recent, promising, self-supervised pre tre-trained EEG representation but was published after our experiments had ended. While our contribution lies representation learning but in an SC-BCI framework that can be applied to any representation, future research could focus on comparing perfor- mance between various EEG representations. Additionally, we leveraged alternative representations to ensure open reproducibility, employing EEGNet to bypass limitations sharing our original dataset imposed by privacy constraints. This enables the sharing of encoded data openly. An EEGNet model from Braindecode [63] was trained on separate dataset specifically for task-relevant P3 classification. Further details and EEGNet results are in Appendix
6.4 Human validation experiments
We conducted two user studies to ground our results against human perceptual judgments. First, via H-Rank, where a user manually scored image similarity compare the algorithm's 's scores with fine-grained human perception. Second, via H-ID, where a larger cohort determined the distance which humans stop perceiving differences between faces. This second study contextualized all downstream experiments without requiring individual experiments for each scenario. Participants and precise experimental details can be found in Appendix C.2
Results
How algorithm and human scores correlate with the similarity function. Figure 2 (left) shows that, in a randomly selected experimental run, CURSOR scores exhibit strong negative correlation with the distance between the hypothesis and target faces, showing a Pearson coefficient R of -0.82 (p < 0.001). These scores also align closely with those manually assigned by human evaluators, achieving an R of -0.97 (p < 0.001). This outcome confirms that CURSOR and H-Rank scores match effectively, correlating well with our similarity metric.
H-D
1.105
Target
Blank
Candidates, Idersificarion
1.100-
R -0.97.
Human
1.1e 102
0.800
1.5
1.5
0.5
y 1...
0.5
0.600
0.4
D.
4 1.090
0.400
Interiitcation
0.3
1.005
0,200
0.2
LR
0.1
1.080
p 7.6e-16
= -0.82.
0.000
0.0
H-Rank
10
20
30
40
top rank (LR)
rank (S-UR)
optimized (LR)
Figure 2: (Left) CURSOR and human scores correlate strongly with similarity. [Blue] Predicted scores (5) (Alg. Twin with LR estimator using full dataset) against ground-truth similarity (d) for 60 face images with regression lines and 95% CI. [Pink] Human-assigned H-RRank scores to sets of 10 facial images morphing away from the target [right axis). (Right) A face at d < 1.6 has an error rate at chance level, suggesting perceptual indistinguishability the target. stimulus at d 2 16.3 has an error rate nearing 0, confirming perceivable differences from the target. These statistics (Wilson Score shading at 95% CI) were collected during H-ID trials [top]. The vertical dotted lines show ranking and optimization performance, both recovering faces indistinguishable from the target.
How human identification rate varíes with image distances. H-ID results (Figure 2 right) confirmed near-chance identification at distances close to zero with ry(0) 0.493 (95% CT: 0.414, 0.573) and (1.6) = 0.503 (95% CI: 0.425, 0,581) indicating perceptual indistinguishabiliy for d < 1.6. At d > 16.3, faces became clearly distinguishable with 1716.)) 0.037 (95% CL: 0.018.1 0.074).
How unsupervised ranking of images match their similarity to the mental target. The predicted scores were utilized to rank hypotheses by their similarity to the mental target, with the Linear Regressor (LRR) performing best across all metrics (Figure and Table The average correlation coefficient between predicted scores and ground-truth distances -0.77 + 0.04 SD for LR.
8

---

indicating that predicted scores effectively rank the hypotheses, with a mean top rank of 6.64 out of 60. At the top rank, our method achieved mean distance of 2.9 + 3.08 SD to the unknown target face, a distance only slightly perceptible to human subjects r7(3.6) 0.377 (95% CT: 0.304, 0.455).
Baseline performance was significantly worse than L.R across all metrics (Mann-Whitney U, p 0.001, Bonferroni corrected), with significant perceptual difference of top ranked faces to the target (Fisher exact test comparing 77(6.) and (7(2177, odds ratio 18.49, p 0.001), confirming that EEG responses are the source of information and CURSOR unsupervised scoring function can convincingly rank hypothetical targets. Additional metrics reported in Appendix and E.4 including top-k accuracies and signal-to- to-noise analysis between methods.
Curre
1.0
1=30)
0.00
(A5D)
1=5D)
4.77
6.04
0.6
3 20.00
13.0
-22
26.5
0.0
dallaa parts
Cauala parts
duda pais
Figure 3: Rank-related performance for different estimators and data sizes (mean + ste, std at N, inv. y-axis). (Left) Correlation coefficient (R) between scores and ground-truth distances. (Middle) Target rank out of 60. (Right) Euclidean distance to the target for top ranked candidate. The bottom dotted line is theoretical random performance. With LR rankings, human subjects could barely distinguish top-ranked faces from the target face with (3366) 0.377 (95% Cl: 0.304. 0.455).
Table LR scores best across metrics, with LR, MLP, and SVR metries significantly better than S-LR and Dummy controls (Mann-Whiney Whitney U, p < 0.001, Bonferroni corrected). The table shows ranking metrics (mean + std) of different estimators and baselines N 9234 in Figure
Model
R
Rank
d top rank
LR
-0.77 = 0.04
6.63 = 5.75
2.90 = 3.08
S-LR
-0.01 - 0.14
27.13 = 18.45
20.85 = 13.48
Dummy
0.01 0.13
27.91 15.95
21.97 = 12.86
MLP
-0.32 +.11
21.68
= 15.48
12.11
= 11.07
SVR
-0.69 - 0.06
16.04 10.38
6.77 = 4.65
How ranking performance varies across estimators and data sizes. The performance of com- parison estimators, SVR and MIP, was consistently lower than the Linear Regressor but above control conditions, confirming that our algorithm works as long the estimator used can improve prediction performance over shuffled versions the dataset. In addition, Appendix Table shows that a modern neural network embedding layer (EEGNet) followed by fully connected network (BestMLP) performs at a level cquivalent to Linear Regressor. We further investigated estimators' performance labeled data versus their performance this task in more detail in Appendix E.22 Based on these results, and given that LR faster to fit, we used only LR for the optimization tasks along with our two baselines (S-LR and Dummy).
How optimization performs in dimensionally reduced spaces. The optimization process success- fully converged to optimal face (Figure 4), positioned at average Euclidean distance of 0.93 from the unknown target face in the original latent space - a difference barely perceptible to human subjects with 171.6) 0.503 (95% CI: 0.425, 0.581). In contrast, the control methods S-LR and Dummy failed to converge, yielding faces at average Euclidean distances of 24.09 and 22.51 from the target face, which are clearly perceptible to human subjects with 1/(2177 0.014 (95% CI: 0.072, 0.032).
Baselines performance significantly worse than LR (Mann-Wer WhinnWy U, p < 0.001, Bonferroni corrected), with significant perceptual difference (Fisher exact test comparing (116)) and 12(2777) odds ratio = 31.00, p < 0.001), confirming that EEG responses are the source of information and CURSOR unsupervised scoring function can successfully guide optimization process.

---

LR
S-LR
Dummy
Target
IR
Ranking
SAR
Dummy
UR
Optimization
SAR
Durny
30
20
10
0
0
500
1000
N iteration
Gremoved
Figure 4: (Left) Optimization converges to < distance to the target. Candidates distance (d) to the target against iteration number (left, mean + std). We can recover near-target faces even when near-target stimuli are absent in the dataset, up to dremoved 10. Best candidates found after 1000 iterations shown against ablation distances dremoned around the target (right, mean + std). (Right) Top-ranked (middle) and optimized (right) images per estimators from one randomly selected run and four randomly selected target faces (left). Images from CURSOR using Linear Regression (LR) are indistinguishable from the target image, while controls S-LR and Dummy exhibit visual differences.
Visual inspection. Figure 4 (right) shows top-ranked and optimized faces for randomly selected runs and target faces between our best estimator (LR) and controls. Images for all 17 targets are included in Appendix Figure g
Recovering ground-truth labels. Once å has been recovered in an unsupervised manner, we can reconstruct each distance via d, - (2,)) for all E M This recovery step is significant because it serves as a proxy for calibration performance across any potential downstream supervised tasks and methods. In other words, if self-calibration can accurately reconstruct the ground-truth labels, then any subsequent supervised model can operate on these calibrated representations as the data had been originally labeled. Our label-free CURSOR pipeline achieves reconstruction accuracy of RMSE 0.18 + 0.08 (Appendix (E.7). For context, a supervised decoder applied to the same EEG task and feature space (29 channels x 7 time windows, 203 features) reports RMSE = 0.17 = 0.09 in (71). despite minor differences in cross-validation meta-parameters.
8 Discussion, Limitations, and Conclusions
We addressed the problem of recovering image targets that a participant has mind using only unlabeled EEG responses recorded while viewing a sequence of unlabeled images in continuous stimuli spaces. We demonstrated that our CURSOR algorithm can address this problem, recovering images indistinguishable from the targets participants had in mind, confirmed by user studies.
Dataset contribution. To our knowledge, we release the first EEG dataset (N 9234) for exploring SC-BCI in continuous domains with stimuli represented in a latent space and participants actively engaging with target in mind.
Limitations. This study is restricted to face stimuli from one pre-trained GAN. Therefore, our experiments were limited synthetic faces and generalization to real existing faces is yet to be explored. The acquisition protocol never explored latent distances > 40 (Euclidean) from the target, and relied on a predefined similarity function in the stimuli latent space. The optimization was run in lower-dimensional latent subspace chosen for computational efficiency using simple heuristics, which may bias optimization. There may be alternatives that can improve our results. Real-time performance and cross-subject generalization remain untested.
Broader impact. Ethical considerations are critical as inferring intent without explicit consent raises privacy concerns 14 17] Working on explainable SC-BCIs and methods to assess confidence in their predictions will play a crucial role. The technology could broaden access to communication for users who cannot provide reliable overt feedback (e.g.. individuals with severe motor impairments) and open new forms of adaptive human-computer interaction. Future work should pursue such benefits while remaining vigilant about misuse.
Further limitations and ethical considerations discussed in detail in the Appendix F
10

---

Acknowledgements
Jonathan Grizou conducted this work during his tenure an Assistant Professor at the University of Glasgow and subsequently through GrizAI Ltd. We gratefully acknowledge the financial support of both organizations.
The research was also partially funded by the the Alfred Kordelin Foundation (grant 230099) and the Finnish Foundation for Technology Promotion (grant 10168). We thank Roderick Murray-Smith helpful advice and access to facilities, and Huawei Cloud R&D Finland for travel support. Computing and storage resources
provided by the Finnish Computing Competence Infrastructure (FCCI; HILE ERC grant ILLUMINATOR, 101 1 14623). We thank Jaakko Lehtinen, Tero Karras, Samuli Laine, and Timo Aila of NVIDIA for providing assistance and advice on GANs.
References
[1] Hamad Ahmed et al. "Confounds in the Data Comments on "Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features " In: IEEE Transactions Pattern Analysis and Machine Intelligence 44.12 (2022), pp- 9217-9220. DOT: 10. 1109/
TPAMI 2021 3121268
[2] Hamad Ahmed et al. "Object Classification From Randomized EEG Trials". In: Proceedings of the IEEF/CVE Conference on Computer Vision and Pattern Recognition (CVPR). June 2021,
pp. 3845-3854.
[3] Takuya Akiba al. Optuna: A Wext-generation Hyperparameter Optimization Framework.
2019. Xiv: 1907 10902 [cs.LGg
[4] Yunpeng Bai et al. "Dreamdiifusion: Generating high-quality images from brain EEG signals".
In: arXiv preprint arXiv:2306. 16934 (2023).
[5] Paul Barde et al. Learning to Guide and to Be Guided in the Erchitect-Buildee Problem. 2022.
arXiv: 2112. 07342 [cs.LG]
[6] Jessica W Berg et al. Informed consent: legal theory and clinical practice. Oxford University Press, 2001.
[7] Hari M Bharadwaj. Ronnie B. Wilbur, and Jeffrey Mark Siskind. "Still an Ineffective Method With
on "Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual "eatures"" In: IEEE Transactions on Pattern Analysis and Machine Intelligence 45.11 (2023), pp. 14052-14054.
SeetrialsERPs-CCmmmee
[8] Benjamin Blankertz. "Gentle introduction to signal processing and classification for single-trial EEG analysis". In: Brain-Computee Interfaces Handbook. CRC Press, 2018, pp. 343-370. [9] Benjamin Blankertz et al. "Single-trial analysis and classification of ERP components tutorial". In: Neurolmage 56.2 12.11). Multivariate Decoding and Brain Reading, pp. 814 825.
ISSN: 1053-8119. DO1: 10. 1016/j neuroimage. 2010. 048
[10] Richard Glen Boire "On Cognitive Liberty (Part I)". In: Center for Cognitive Liberty and Ethics 1.1 (2000), pp. 7-13. ISSN: 1527-3946.
[11] David Cohn, Les Atlas, and Richard Ladner. "Improving generalization with active learning".
In: Machine learning 1 15.2 (1994), pp. 201-221.
[12] Paul G Constantine, Eric Dow, and Qiqi Wang. "Active subspace methods in theory and practice: applications to kriging surfaces". In: SIAM Journal on Scientific Computing 36.4 (2014), A1500-A1524.
[13] Laming D. "Fechner's law: where does the log transform come from?" In: Seeing Perceiving 23 (2 2010), pp. 155-71. DOL: 10.
[14] Keith Davis and Tuukka Ruotsalo. "Physiological Data: Challenges for Privacy and Ethics". In: arXiv preprint arXiv:2405.152: 0et.24055.5277 (2024).
[15] David Eriksson et al. "Scalable global optimization via local Bayesian optimization". In: Advances in neural information processing systems 32 (2019).
[16] Lawrence A. Farwell and Emanuel Donchin. "Talking off the top of your head: toward mental prosthesis utilizing event-related brain potentials". In: Fleectreencephalogrrahyy and clinical
Neurophysiology 70.6 (1988), pp. 510-523, DO1: (64

---

[17] Lawrence A. Farwell and Emanuel Donchin. "The Truth Will Out: Interrogative Polygraphy ("Lie Detection") With Event- ReeateRelated Brain Potentials" In: Psychophysiologg 28.5 (1991).
pp. 531-547. DOT: 1 1111/j 1469-8986 1991. tb01990
[18]
Timnit Gebru et al. "Datasheets for datasets". In: Commun. ACM 64.12 (Nov. 2021), pp. 86-92.
ISSN: 0001-0782. DOI: [0. /145/3387/233
[19] Alessandro Т. Gifford et al. "A large and rich EEG dataset for modeling human visual object recognition". In: Neurolmage 264 (2022), p. 119754, ISSN: 1053-8119, DO1: 10 1016/]
neuroimage. 2022. 119754
[20] Ian J. Goodfellow et al. "Generative Adversarial Nets". In: Proc. NIPS. 2014, pp. 2672-2680.
[21] Alexandre Gramfort et al. "MEG and EEG Data Analysis with MNE-Python".. In: Frontiers in 00267
Neuroscience 7.267 (2013), pp. 1-13. DOI: 10 3389/ffinssn..
[22] Caterina Gratton et al. "Attention selectively modifies the representation of individual faces in the human brain" In: Journal of Neuroscience 33.16 (2013), pp. 6979-6987
[23] Jonathan Grizou, Interactive introduction to self-calibrating interfaces. 2022. arXiv: 2212
05766 [cs.HC]
[24]
Jonathan Grizou, Manuel Lopes, and Pierre- Oudeyer. "Robot learning simultaneously a task and how to interpret human instructions". In: 2013 IEEE third joint international
conference on development and learning and epigenetic robotics (ICDL), IEEE. 2013, pp. 1-8. [25] Jonathan Grizou et al. "Calibration-free BCI based control", In: Proceedings of the AAAI Conference on Arlificial Intelligence. Vol. 28. 2014.
[26] Tijl Grootswagers et al. "Human EEG recordings for 1,854 concepts presented in rapid serial visual presentation streams". In: Scientific Data 9.1 (2022), p. 3. ISSN: 2052-4463, DOI:
10. 000.138//41157-021--000 01102-77
[27] Sven Guenther, Nataliya Kosmyna, and Pattie Maes. "Image classification and reconstruction from low-density EEG In: Scientific Reports 14.1 (July 2024), p- 16436. ISSN: 2045-2322.
D0.: 10t.100.138//41111 1e1.:1888/41118-00 024 -1224-6622111
[28] Nikolaus Hansen. "The CMA evolution strategy: a comparing review". In: Towards a new evolutionary computation: Advances in the estimation of distribution algorithms (2006),
pp. 75-102.
[29] Erik Härkönen et al. "Ganspace: Discovering interpretable gan controls". In: Advances in neural information processing systems 33 (2020), pp. 9851-9850.
[30] David Hübner et al. "Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees". In: PloS one 12.4 (2017), e0175856.
[31] David Hüebner et al. "Unsupervised learning for brain-computer interfaces based on event- related potentials: Review and online comparison [research frontier]". . In: IEEE Computational Intelligence Magazine 13.2 (2018), pp. 66-77.
[32] Iñaki Iturrate al. "Exploiting task constraints for self-calibrated brain-machin interface control using error-related potentials". In: PloS one 10.7 (2015), e0131491.
[33] Mainak Jas al. " "Automated rejection and repair of bad trials in MEG/EEG". In: 2016 International Workshop on Pattern Recognition in Neuroimaging (016, pp. 1-4. DOI:
10. 1109/PRNI 2016. 7552336
[34] Mainak Jas al. " "Autoreject: Automated artifact rejection for MEG and EEG data". In: Neurolmage 159 (2017), pp. 417-429. ISSN: 1053-8119. DO1: 10. 1016/ neuro oimaee
2017.06.05 030
[35] David Alexander Kahn et al. "Temporally distinct neural coding of perceptual similarity and prototype bias". In: Journal of Vision 10.10 (2010), pp. 12-12.
[36] Lauri Kangassalo, Michiel Spapé, and Tuukka Ruotsalo. "Neuroadaptive modelling for gen- erating images matching perceptual categories". In: Scientific Reports 10.1 (2020), p. 14719. ISSN: 2045-2322. DO1: 10. 1038/ -41598- 020-71287-1
[37] Tero Karras et al. "Progressive Growing of GANs for Improved Quality, Stability, and Varia- tion". In: Proc. ICLR. 2018.
[38] Pieter-Jan Kindermans, David Verstraeten, and Benjamin Schrauwen. "A bayesian model for exploiting application constraints to enable unsupervised training of a P300-based BCT". In: PloS one 7.4 (2012), e33758.
12

---

[39] Spyros Kokolakis. "Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon". In: Computers & Security 64 (2017), pp. 122-134. ISSN: 0167-4048. DOT: 10. 1016/ cose. 2015 07 002
[40] Vernon J Lawhern et al. "EEGNet: a compact convolutional neural network for EEG-based prain-computer interfaces". ". In: Journal of neural engineering 15.5 (2018), p. 056013.
[41] California State Legislature. California Privacy Rights Act (CPRA). Amending the CCPA, often referred to as "CCPA 2.0". 2020.
[42] Steven Lemm et al. "Introduction to machine learning for brain imaging". : In: Neurolmage
56.2 1). Multivariate Decoding and Brain Reading, pp. 387-399, ISSN: 1053-8119, DOI:
10. 1016/j nwwroiiaageeeeee 2010. 11. 004
[43] David A Leopold et al. "The dynamics of visual adaptation to faces". In: Proceedings of the Royal Society B: Biological Sciences 272. (2005), pp. 897-904.
[44]
Ben Letham al. "Re-examining linear embeddings for high-dimensional Bayesian optimiza- tion". In: Advances in neural information processing systems 33 (2020), pp. 1546-1558. Ren Li et al. "The Perils and Pitfalls of Block Design for EEG Classification Experiments", In: IEEE Transactions on Pattern Analysis and Machine Intelligence 43.1 (2021), pp. 316-333. DOL: 10. 1109/TPAMII TPAMI. 2020. 2973153
[45]
[46] Bradley Malin, Khaled El Emam, and Christine M O'Keefe. "Biomedical data privacy: problems, perspectives, and recent advances". . In: Journal of the American Medical Informatics
Association 20.1 (Jan. 2013), pp. 2-6. ISSN: 1067-5027.
DOI: 10. 1136/ amiajnn- - 2012 2012
001509
[47]
Kathryn McConkey et al. IFTT-PIN: A Self- Calibratnnt PIN-Enary Method. 2024. arXiv: 2407. 02269 [cs.HC]
[48]
Margaret Mitchell et al. "Model Cards for Model Reporting". In: Proceedings of the Conference on Fairness, Accountability, and Transparency. FAT* 19. Atlanta, GA, USA: Association for
Computing Machinery, 2019, pp. 220-229. ISBN: 978 9781450361255. 3287596
DOL: 10. 1145/3287566
[49]
Helen Nissenbaum. "Privacy as contextual integrity". In: Wash. Rev. 79 (2004), p. 119.
[50]
Jorge Nocedal and Stephen Wright. Numerical optimization. Springer, 2006. Kento Nozawa and Issei Sato. "Empirical evaluation and theoretical analysis for representation
[51]
learning: survey". In: arXiv preprint arXiv:2204. 088266 (2022).
[52] P Ohm. Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization '57 UCLA Law Review 1701 (2010) 1701-1711. 2010.
[53] European Parliament and the Council of the European Union. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). Official Journal L 119, 4 May 2016. p. 1-88. 2016
[54] European Parliament and the Council of the European Union. Regulation (EU) 2024/1689 of the European Purliament and of the Council of 13 June 2024 laying down hurmonised rules on artificial intelligence and amending various regulations and directives (Artificial Intelligence Act). Official Journal L, 2024/1689, 12 July 2024, with EEA relevance. 2024.
[55] L. C. Parra et al. "Recipes for the linear analysis of EEG". In: Neuroimage 28.2 (Nov. 2005),
pp. 326-341. DO1: 10. 1016/ www.oinwge....... 2005. 05. 032
[56] F. Pedregosa et al. "Scikit-learn: Machine Learning in Python". In: Journal of Machine Learning Research 12 (2011) 1). pp. 2825-2830.
[57] W. Nicholson Price and I. Glenn Cohen. "Privacy in the age of medical big data". In: Nature
Medicine 25.1 (2019). pp. 37-43, ISSN: 1546-170X. D01: 10. 1038/ s41591- 11151-118-027721
[58] María Luisa Barragán Pulido et al. "Alzheimer's disease and automatic speech analysis: review". In: Expert systems with applications 150 (2020), p. 113213.
[59] Novi Quadrianto et al. "Estimating labels from label proportions". In: Proceedings of the 25th international conference on Machine learning. 2008, pp. 776-783.
[60] Nona Rajabi et al. "Mental face image retrieval based on a closed-loop brain-computer interface". . In: International Conference Human- Commnt-r Interaction. Springer. 2023, pp. 26-45.
13

---

[61] Siddharth Reddy, Sergey Levine, and Anca Dragan. "First contact: Unsupervised human- machine co-adaptation via mutual information maximization". In: Advances in Neural Infor- mution Processing Systems 35 (2022), pp. 31542-31556.
[62] Pia Rotshtein et al. "Morphing Marilyn into Maggie dissociates physical and identity face representations in the brain". In: Nature neuroscience 8.1 (2005), Pp. 107-113.
[63] Robin Tibor Schirrmeister et al. "Deep learning with convolutional neural networks for EEG decoding and visualization". In: Human Brain Mapping (Aug. 2017). ISSN: 1097-0193, DOI: 10. 10../hb2bbb..mmmm 23730
[64] Wrye Sententia. "Neuroethical Considerations: Cognitive Liberty and Converging Technolo- gies for Improving Human Cognition", In: Annals of the New York Academy of Sciences 1013.1 (2004), pp. 221-223 DO1: 10. 1196/ annalsn 1305. 014
[65] Yujun Shen and Bolei Zhou. "Closed-form factorization of latent semantics in gans". In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021,
pp. 1532-1540.
[66]
Daniel J Solove. "A taxonomy of privacy". In: U. Pa. I. Rev. 154 (2005), p. 477.
[67]
Jan Sosulski and Michael Tangermann. "UMM: Unsupervised mean meef-differrenc maximization". In: arXiv preprint (rXXfv2306.11883 (2023).
[68] Michiel Spapé, Rinus Verdonschot, and Henk van Steenbergen. The E-Primer: An introduction
to creating psychological experiments in E-Prime. Second edition updated for E-Prime 3. 2nd ed. Leiden, The Netherlands: Leiden Univerisity Press, 2019.
[69] Abdulhamit Subasi and M. Ismail Gursoy. "EEG signal classification using PCA, ICA, LDA and support vector machines". In: Expert Systems with Applications 37.12 (2010), pp. 8659-
8666. ISSN: 0957-4174. DO1: 10. 1016/ j. eswa. 2010. 06.065
[70] Jordy Thielen, Jan Sosulski, and Michael Tangermann. "Learning to walk on new ground: Calibration-free decoding for V-P BCT", In: arXiv preprint arXiv:2403. / (2024). [71] C. de la Torre-Orth and Ruotsalo. "Perceptual Visual Similarity from EEG: Prediction and Image Generation". In: Proceedings of the 32nd ACM International Conference on Multimedia (MM '24). Melbourne, VIC, Australia: ACM, 2024, October 28-November 1. ISBN: 979-8-
4007-0686-8/24/111 DOI: 10. 1145/3664647 3664647 3685508
[72] C. de la Trtiz-Ortiz, M. Spapé, and T. Ruotsalo. "The P3 indexes the distance between perceived and target image". In: Psychophysiology 60.5 (May 2023), e14225.
[73] Tim Valentine. "A unified account of the effects of distinctiveness, inversion, and race in face recognition".. In: The Quarterly Journal of Experimental Psychology Section A 43.2 (1991), pp. 161-204.
[74] Thibault Verhoeven et al. "Improving zero-training brain-computer interfaces by mixing model estimators". In: Journal of neural engineering 14.3 (2017), p. 036021.
[75]
Pauli Virtanen et al. "SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python".
In: Nature Methods 17 (2020), pp. 261-272 DO1: 10. (4138//41592-00 019 -086--2
[76] Anna-Lisa Vollmer et al. "Studying the co-construction of interaction protocols in collaborative tasks with humans". In: 4th International Conference on Development and Learning and on Epigenetic Robotics. IEEE. 2014, pp. 208-215.
[77] Guangyu Wang et al. "EEGPT: Pretrained Transformer for Universal and Reliable Repre- sentation of EEG Signals". In: The Thirty-eighth Annual Conference Neural Information Processing Systems. 2024.
[78] Andrew B Watson and Denis G Pelli. "QUEST: A Bayesian adaptive psychometric method"'
In: Perception & psychophysics 33.2 (1983), pp. 113-120.
[79] William F Whitney et al. "Evaluating representations by the complexity of learning low-loss (2020).
predictors". In: arXiv preprint
[80] Felix A Wichmann and N Jeremy Hill. "The psychometric function: I. Fitting, sampling, and goodness of fit". In: Perception & psychophysics 63.8 (2001), pp. 1293-1313.
[81] John Williamson and Roderick Murray-Smith. "Pointing without a pointer". In: CHI'04 Extended Abstracts on Human Factors in Computing Systems. 2004, pp. 1407-1410.
[82] Tengyang Xie et al. "Interaction Interaction-grounded learning with action-inclusive feedback". In: Ad- vances in Neural Information Processing Systems 35 (2022), pp. 12529-12541.
14

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: From the Abstract, claim (1) is supported by Figure 2 (left), (2) is supported by Figure 3 (3) by Figure (right) and From the Introduction, Contribution 1 is covered in Sections 3 and 4, and the dataset (as supplementary material), Contribution 2 and 3 in
Section 7 and the Appendix.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
It is fine to include aspirational goals as motivation as long as is clear that these goals attained by the paper.
2. Limitations
Question:
Does the paper discuss the limitations the work performed by the authors?
Answer: [Yes]
Justification:
We have discussed the limitations in Sections 4, 6 and 8, and Appendix G.
Guidelines:
The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create separate "I "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect the scope of the claims made, e.g., if the approach was only tested on a few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution low or images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and prool's
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
15

---

Answer:
[NA]
Justification:
The paper does not include theoretical assumptions or proofs.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Question:
Answer: [Yes]
The main text and the Appendix contain all the information, including our hard- ware specifications. Further details can be found in the code and data in the supplementary material.
Justification:
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is novel architecture, describing the architecture fully might suffice, or if the contribution a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility instructions for how to replicate the results, access to a hosted model (e.g.. in the case of large language model), releasing of a model checkpoint, other means that appropriate to the research performed.
also be provided via detailed
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.
(b) the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
(c) If the contribution is a new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).
(d) We recognize that reprodueibility may be tricky in some cases, in which authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers have some path reproducing or verifying the results.
5. Open access to data and code
16

---

Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, as described in supplemental material?
Question:
Answer: [Yes]
Justification:
The code and data provided as supplementary material.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurlPS code and data submission guidelines (https://niis.co cc/
for more details.
While we encourage the release of code and data, we understand that this might not be possible, "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurlPS code and data submission guidelines (https
/ //nips. ctipps.cc/uublic//un guwdes/ddeeeee CooeeSumeeeeeeeeeeeeee ssi onyylicy) for more details.
The authors should provide instructions on data access and preparation, including how access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only subset of experiments reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Question:
Answer: [Yes]
Justification: Data splits, hyperparameters, and the rationale for their selection are included in Section 6 and complemented Appendix. Very specific details, such as seeds, can be found in the code.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented the core the paper to a level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, or as supplemental material.
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer: [Yes]
Justification: Error bars were provided for all experiments, and statistical tests were reported for all claims.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by emor bars, confi- dence intervals, or statistical significance tests, least for the experiments that support the main claims of the paper.
17

---

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
It should be clear whether the error bar is the standard deviation or the standard error of the mean.
OK to report 1-sigma error bars, but one should state The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that out of range (e.g. negative error rates).
If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed reproduce the experiments?
Question:
Answer: [Yes]
Justification: In Appendix C.3
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics
Answer: [Yes]
Justification: We followed experimental protocols and got approval from ethical review board in humanities and social and behavioral sciences of the University of Helsinki, and/or the College of Science and Engincering Ethics Committee of the University of Glasgow. Possible societal impact and potentially harmful consequences are discussed in Section and Appendix F22 with relevant references.
Guidelines:
The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g.. there special consid- eration due to laws or regulations in their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: Possible positive and negative societal impacts and potentially harmful conse- quences are discussed in Section and Appendix F2 with relevant references.
18

---

Guidelines:
The answer NA means that there is no societal impact of the work performed.
If the authors answer NA or No. they should explain why their work has no societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied partieular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point that improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition attacks, mechanisms for monitoring misuse, mechanisms to monitor how system learns from feedback over time, improving the efficiency and accessibility of MI..
11. Safeguards
Does the paper deseribe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification: We do not release models. Data are fully anonymized as per our ethical review board in humanities and social and behavioral sciences of the University of Helsinki, and/or the College of Science and Engineering Ethics Committee of the University of Glasgow protocols. We did not identify high risks of misuse of this work in its current form.
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards challenging, and many papers do not require this, but encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [NA]
Justification: We collect our own data and follow all protocols and approval processes from our ethical review board in humanities and social and behavioral sciences of the University of Helsinki, and/or the College of Science and Engineering Ethics Committee of the University of Glasgow.
Guidelines:
19

---

The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version of the asset is used and, if possible, include URL.
The name of the license (c.g., CC-BY 4.0) should be included for each asset. For scraped data from a particular source (e.g.. website), the copyright and terms of service of that source should be provided.
If assets released, the license, copyright information, and terms of in the package should be provided. For popular datasets, papprssithcaseeo thcode com/ datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re- re-packaged, both the original license and the license of the derived asset (if has changed) should be provided. If this information is not available online, the authors are encouraged to reach out to the asset's s creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification: The dataset and code released documented with license, and details of the data acquisition protocols and data anonymity were performed per our institution's s guidelines and were approved for release.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/moodll as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset used.
At submission time, remember to anonymize your assets (if applicable). You can either create anonymized URL or include an anonymized zip file.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [Yes]
Justification: We include the verbatim instructions for the study participants as well as their compensation (cinema vouchers) Appendix A
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included the main paper.
According to the NeurlPS Code of Ethics, workers involved data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
20

---

Answer: [Yes]
The study protocol involving human participants was reviewed and approved by the ethical review board in humanities and social and behavioral sciences of the University of Helsinki, and/or the College of Science and Engineering Ethics Committee of the University of Glasgow, and all participants signed written consent (Appendix A
Justification:
Guidelines:
The answer NA means that the paper does not involve crowdsoureing nor research with human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and expect authors adhere the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Question: Does the paper describe the usage of LLMs if it is important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, formatting purposes and does not impact the core methodology. scientific rigorousness, or originality of the research, declaration is not required.
Answer: [NA]
Justification: The research does not involve LLMs any important, original, or non- atandtaddrrd components.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to LLM policy for what should or should not be described.
21

---

Appendix
A Neurophysiological Data Acquisition
Participants. The Ethical Committee of the University of Helsinki approved the study protocol, and participation in the neurophysiological experiment was advertised to university population. In total, 31 participants volunteered for the study, aged 23.5 3.5 (mean + SD), from which 13 self-identified as male, 18 as female, and none diverse. All participants were informed of the study's s purpose and provided written consent before participation. After completing the study, participants were compensated with cinema vouchers.
RSVP task Subsequently, the Rapid Serial Visual Presentation (RSVP) block started, presenting one facial image a time every 500 ms using E-Prime [68]. To minimize visual variance unrelated to facial features, gray ellipsoid mask of 746 x 980 pixels was applied to each image. Each trial included 70 images: 28.5% related images and 71.5% unrelated images, where the imbalance is intended to enable the "oddball" paradigm. This design avoids confounds arising from participants attempting to predict the following image. It also facilitates the processing of data into overlapping epochs, therefore balancing acquisition speed with data quality [16). The order of the images was randomized for each trial but ensuring that each presentation of related image was followed by to 8 unrelated images. In total, each participant completed 17 trials, therefore displaying 1120 images generated from the 17 image targets. Data acquisition sessions, including setup and EEG recording, lasted approximately hour.
The participants received the following instructions before starting the task:
In this block you will be asked to concentrate and mentally count every time you see this person below. [Source image shown: young dark-haired female] (Participant clicks the image]
That means you should ignore people are not this person. For example, ignore people such as this: [Flanker image shown: young blond female, different identity than source]
[Participant clicks
image]
Pictures of persons will be shown. If the person is the one now shown in the middle, count it. If the person looks different (e.g. like the ones the side), ignore it. Click on the middle picture to begin!
[Three images shown: flanker, source, flanker; same respective identities as before] [Participant clicks the central image]
You will be shown faces. Silently count the relevant ones.
Press SPACE to begin
[RSVP begins]
[Three images: flanker (constant), changing image, flanker (constant)] [70 central images shown]
How many did you count?
You will receive a prize if your count is closest to the correct answer! [Text input field]
[Participant types the count]
22

---

Thank you for participating and goodbye! The iment will close in 4 seconds.
EEG recording. The experimental setup used a 32-channel EasyCap system, utilizing Ag/AgCI electrodes in the 10-20 system placed at FPI, FP2, F7, F3, Fz, F4, F8, FT9, FC5, FCI, FC2, FC6, FTIO, T7, C3, Cz, C4. T8, TP9, CP5, CPI. CP2, CP6, TP10, P7, P3, Pz, P4, P8, Ol, 02, and Iz, with AFz as ground. The participants seated approximately 60 em away from 24" LCD screen with resolution of 1920 x 1080 pixels and a refresh rate of 60 Hz.
The EEG responses
captured utilizing BrainProducts QuickAmp USB amplifier, with voltages digitized via Brain Vision Recorder at a sampling rate of 1000 Hz and using 0.01 Hz high-pass filter. During recording, signals were re-referenced the common average. Ellecrooculoograhic (EOG) signals were recorded using bipolar electrodes, positioned in the peripheral area surrounding both eyes. Specifically, one pair was placed laterally to both eyes, while another pair was positioned above and below the right eye. The resulting signals are a time series of voltages distributed across channels an e é tensor (EEG events, 32 channels, time).
EEG filtering and artifact rejection. EEG responses were processed offline using MNE for Python [21]. Signals were filtered using a 0.1 Hz high-pass filter and a 50 Hz low-pass filter. Then, the segmented into time-locked bands, epocks, each lasting and starting 0.2 prior to the onset of each image stimulus [16]. Epochs were then baseline-corrected by subtracting the mean voltage per channel.
Artifact rejection was performed by eliminating epochs with a voltage exceeding + 400 μ in channels F3. Fz, F4, FC1, FC2, C3, Cz. C4, CP1, CP2, P3, Pz, and P4. Following, they applied Indepen- dent Component Analysis (ICA) to remove further artifacts such as eye movements. For this, they 5.3% epoch rejection rate. To expedite downstream com- putations, EEG responses were finally resampled to 250 Hz. Unlike its default behavior, Autoreject was configured to drop defective epochs rather than repair them. Channels with data corruption were dropped across all participants, reducing the number of channels from 32 to 29.
employed Autoreject (3 34 resulting
The resulting and unit variance across trials for each participant, and across participants, to reduce inter-trial and inter-participant variability. EEG was windowed into seven equidistant time windows between 50 ms and 800 ms post-stimulus onset to streamline further and denoise the data. This yields an X x (EEG events, 29 channels, 7 time windows) tensor (i.e. 29 7 203 features per EEG event). CURSOR used flattened representations of the resulting EEG tensor, concatenated with image embeddings.
X tensor (EEG events, 29 channels, time) was then standardized to zero mean
B Optimizing with Dimensionality Reduction.
The selection of appropriate dimensionality for PCA reduction was guided by the analysis of Pearson correlation statistics across varying numbers of principal components. Figures 5a and [b illustrate the justification for our choices of 20 and 10 dimensions for EEG and face embedding reduction, respectively.
EEG Dimensionality Reduction. Figure 5a displays the relationship between the number of PCA components and the Pearson correlation statistic ranking experiments. Linear Regression method demonstrates gradual improvement that plateaus around 20 components. This asymptotic behavior suggests that 20 dimensions capture the majority of the relevant variance in the EEG data.
Face Embedding Dimensionality Reduction. Similarly, Figure 5b presents the PCA analysis for face embeddings. Linear Regression method demonstrates gradual improvement that plateaus around 10 components. This indicates that a 10-dimensional representation sufficient to retain most of the informative features of the face embeddings.
23

---

PCA FFG- sterliirs
comgenencs
-05-
04
rette namm
intiaa rere: Licerfegrtatee Conmydoating
Stufffe Linerfagress
DammyyCoving Mean
0.2
-02
35.0
(a) Dimensionality reduction of EEG via PCA. The Linear Regression method reaches asymptoti performance 20 components our correlation met- ric.
(b) Dimensionality reduction of face embeddings via
PCA. With EEG reduced to 20 D, the Linear Regres- sion method reaches asarposyyiiotic performance 10 components.
Figure 5: PCA component sweeps for (Left) EEG signals and (Right) face embeddings.
In both cases, the control estimators (Dummy and S-LR) consistently showing little no correlation across all component numbers, hence no leakage of information despite significant dimensionality reduction.
These analyses support our decision to reduce EEG data to 20 dimensions and face embeddings 10 dimensions, as these choices balance dimensionality reduction with preservation of relevant information, as evidenced by the near-asymptotic performance in correlation metrics.
C Experiments
C.1 Comparative and ablation studies
Estimators: Hyperparameters and Search. We used default estimators' meta- meta-parameterss parameters with SVR using the radial basis function (RBF) kernel, and the MLP consisting of 3 hidden layers with (100, 50, and 25) neurons and ReLU activation function. The learning rate for the MLP was to 0,001, and the Adam optimizer used betas set at 0.9 and 0.999.
Additionally, conducted exhaustive hyperparameter search using "GridSearchCV" in scikit- leamn (66 to optimize our Multi-Layer Perceptron (Best MLP) and SVR (Best SVR) models.
For the MLP estimator, we explored activation functions (Identity, ReLU), regularization parameter (0.0001, 0.01, 0.1, 1), hidden layer architectures ranging from simple (100), to comparatively more complex (1000, .... 1000) up to 10 layers, and adaptive learning rates for all experiments. Models were evaluated using cross-validated mean RMSE scores.
For the SVR model, we explored the regularization parameter C' (0.001, 0.01, 0.1, 1), kernel coefficient 7 ("scale"" "auto"), and kernel type (linear, RBF). total, combinations were evaluated.
Data and distance ablations. We evaluated algorithm performance with varying dataset sizes (N) of [9234, 9000, 8000, 7000, 6000, 5000, 4000, 3000, 2000, 1000, 500, 100] to evaluate how the model performance is impacted by the amount of observed data. Each dataset was down-sampled to its final size using uniform random sampling without replacement.
We tested sensitivity to near- near-targe target stimuli by removing stimuli-response pairs below various distance thresholds from the target face [0, 5, 10, 15, 20, 25, 30, 35, 40). This reduced dataset size to, respectively, [9234, 6644, 5432, 4335, 3413, 2668, 2047, 1452, 806]. To control for dataset size effects, we ran parallel experiments with uniformly down-sampled datasets matching each threshold's size.
24

---

Optimization ablation and control. We conducted this ablation studies to investigate the im- portance of tear-target stimuli on CURSOR's performance. Because this removed data from the stimuli-response set, our experiment also impacted data scarcity. To control for dataset size effects, we ran control experiments with uniformly down-sampled datasets matching each near-target ablation threshold's size. Figure illustrates the results of these experiments (mean + SD, x-axis inverted).
806
2047
2668
3413
4335
5432
6644
9234
20
from target
15-
Method Run
Linear Regression (ablation)
SinearRegression (control)
Shuffle Shuffle__inearfeen Linearfeeression
10.
DummySccrrigg Mean
40
30
15
d_ d_removed
Figure Comparison of CURSOR performance under near-target ablation and uniform data reduction conditions. Figure shows distance to target of candidates found after 1000 iterations against dataset ablated within specified distances dremoved around the target (mean SD). We can recover target faces even when near-target stimuli are absent in the dataset, up dremaved 10, demonstrating CURSOR's ability to infer beyond its observation space and robustness to data searcity. The disparity in performance between ablation and control conditions at comparable N values highlights the importance of near-target stimuli for CURSOR's 's accuracy.
Estimator performance on supervised tasks CURSOR Performance. We trained various estimators on ground truth data to estimate their theoretical capacity for predicting d from e. The root mean square error (RMSE) is used as performance metric, with lower values indicating better performance. The primary objective of this study is to investigate whether CURSOR's effectiveness dependent on having highly accurate estimator, and explore the relationship between the estimator's s capacity to capture data relationships and its performance within the CURSOR framework.
C.2 Human validation experiments
H-Rank Ranking face similarity with unlimited time. The H-Rank experiment evaluates the ability to manually assign distance scores and rank images from single latent trajectories between the target and the farthest image used the EEG experiment. An evaluator, uninvolved the EEG exper- iment and sourced from a university population, was presented with sets of 10 progressively morphed images spanning distances from 0 to 40.16 (D = 1.6. 1.6, 3.6, 6.8, 10.9, 16.3, 21.7, 29.4, 36.7, 46.2) (Figure@]iggh right) and the scale was normalized between O and I for ease of evaluation. The remaining images were unlabeled and displayed in a randomized order. The task was not timed, and 10 images per set were displayed simultaneously In total, the evaluator assessed 170 images, corre- sponding to 10 images per each the 17 targets. An alternative representation of the results from the main paper is shown in Figure 7
25

---

Human vs Algortihm Scores
1.110
1.105
Algorthim Scores
Distance to Target
1.095
1.090
1.085
1.080
0.0
0.2
0.4
Human Scores
Figure 7: Human scores attached to a subset of test faces (unlimited time) vs Algorithm score from our method using Linear Regressor on full dataset (error bars show the standard deviation). Each dot represents a test face colored by the distance this face is from the target face. Our algorithms scores correlates strongly with human scores.
H-ID Face identification from short exposure. The H-ID experiment aims contextualise results by determining the distances in Z at which two faces are indistinguishable or become clearly differentiable by human evaluators.
During H-ID trials, target face z" was presented centrally for 1.5 s, followed by 1-second blank interval. Then, two faces comparison face 2g at a distance dz. = Pz- (Ze) from the target. The 0.5 s exposure duration was chosen match the stimulus presentation time used to acquire the dataset in our neurophysiological experiments. Participants were instructed to click on the face they believed matched the target face shown previously.
displayed side-by-side for 0.5 the target face 2* and
For each trial, the target face was selected randomly from the 17 target faces available in our dataset. The position of the target face in the selection stage (left right) was randomized across trials. The distance dz. between the target and comparison face was randomly chosen within the set as for H-Rank. Each participants completed 30 such trials. A local machine used in the setup, using Flask as backend and HTML/C$S/JS for the frontend.
53 participants volunteered for the study, aged between 20 and 39 years (mean 24.5, =D which self-identified female, 45 as male, and none diverse. participants were informed the study's 's purpose, reported no visual impairments, and provided written consent before participation.
= 3), of
We calculated the identification error rate (d...) as the proportion of incorrect selections at each distance dz., using Wilson score for confidence intervals. At dz. = 0, we expect chance performance (error rate 0.5), while for clearly differentiable faces, the error rate should approach 0. II-I was designed to reveal the transition between these extremes.
C.3 Computational environment
The computations performed locally GNU/Linux and MacOS-barte machines:
26

---

A Lenovo Thinkpad t480 running Ubuntu 22.04, an i5-7300U Intel CPU at a base frequency of 2.6 GHz, Intel HD Graphics 620, and 32GB 2400MHz DDR4 RAM;
A custom machine running Linux 6. 10.3, an AMD Ryzen 7840U 16 core base frequency of 3.3 GHz with Radeon 780M Graphics, and 64 GB DDR5-5600 RAM;
A MacBookPro running MacOS Sonoma 14.3, Apple MI Max with 10 cores and 64GB RAM.
Most computationally intensive tasks were executed on an on on-premises AlmaLinux 8.7 cluster with a SLURM scheduler and AMD EPYC 7452 CPUs. Computations utilized embarrassingly parallel tasks, each running on a single CPU core. Jobs were run nodes designated for "short" or "medium" tasks, suggesting relatively low HPC resource demands per job. We launched one job array per estimator due to varying resource requirements Initial computational load was assessed with a job configuration of CPU core, 800 MB of RAM, and walltime of 1 hour, and adjusted based on observed resource usage. Maximum RAM usage and walltime, with least a 20% buffer for variability, were 400 MB and 4 hours for Linear Regression, 400 MB and 4 hours for MLP, and 600 MB and hours for SVR.
There are no specific package version requirements. We use Python 3.11 the oldest stable version at submission time supporting advanced enumerations for quality of life improvements, and therefore dependency versions are flexible. Details on the packages can be found in the dependencies file in the code.
D Alternative EEG Representations
EEGNet model was trained on a separate dataset specifically for visual saliency detection using P3-based binary classification. The EEG data was collected using the same equipment and GAN as in the main experiment to minimize confounds, drift, and noise differences.
D.1 Neurophysiological experiment for visual saliency in faces
After obtaining approval from the Ethical Committee of the University of Helsinki, recruited 31 participants from a University population. Images were generated using the same GAN architecture trained on dataset of celebrity faces in the main experiment, sampled from 511-ddimensional multivariate normal distribution. These images were manually categorized into eight visual categories: blond, dark-haired, female, male, old, young, smiling, and non-smiling. The study apparatus, including the EEG recording setup and stimuli presentation, was consistent with the main experiment.
After providing informed consent, participants in rapid serial visual presentation (RSVP) sequence of 70 images contained one of the eight salient features (target) while maintaining mental count. Each salient feature presented in RSVP trials before switching to the next category. The order of target and non-target images was randomized, in the main experiment.
instructed to identify whether each face shown
Following data collection, EEG data was preprocessed offline using a high-pass filter at 0.2 Hz and a low-pass filter at 35 Hz. The data was then epoched and baselined in the main experiment, with artifacts removed using a threshold of +400 μV based on the highest absolute maximum voltage. This preprocessing step removed two participants due to artifacts and approximately 11% of epochs, resulting in a dataset with an average of 3251 epochs per participant.
D.2 Learning task and prediction setup
EEGNet models were trained to classify the presence or absence of salient features in face images. Each model utilized EEG epochs containing 29 channels and 376 time points as input, with a binary classification label as the output. The training process spanned 10 epochs with batch size of 32, employing a binary cross-entropy loss and the Adam optimizer with learning rate of 0.001 and moment betas set to 0.9 and 0.999. For evaluation, data corresponding to one salient visual category was left out during training. After training, the final classification layer of the EEGNet model was removed, and the remaining network was used to generate embeddings using EEG data from the main experiment input.
27

---

E Results
E.1
Comparative and ablation studies
Estimators: Hyperparameter search. We ran hyperparameter optmizatiin to identify the best- performing MLP and SVR models for the CURSOR framework ("Best MLP" and "Best SVR").
For the Best MLP model, identity activation generally outperformed ReLU, with moderate alpha values (0.1) yielding better results. Simpler architectures (2-3 layers) often performed comparably to or better than more complex ones. The best- pessfrerfonnng configuration consisted of identity activation, alpha 0.1, and two hidden layers of size 100 with an adaptive learning rate. This configuration achieved a mean RMSE score of 0.9138 + 0.0142 (mean + SD).
For the Best SVR model, linear kernels outperformed RBF kernels. Performance was relatively stable across C' values for linear kernels, with the best performance at = 0.01. The "scale" and "auto" y settings produced identical results for linear kernels. RBF kernels showed lower performance, suggesting linear separability in the feature space. The best-performing configuration was = 0.01, y "scale", and the linear kernel, achieving mean RMSE score of 0.9279 0.0115 (mean = SD).
E.2 Estimator performance supervised tasks CURSOR performance
Aligned vs. Shuffled Performance. Figure Sa presents scatter plot comparing the RMSE of each estimator on aligned and shuffled datasets. Baseline methods exhibit equal performance on both datasets, as expected. Despite displaying the lowest absolute performance, the MLP shows relative improvement over the shufiled dataset, allowing it to outperform baselines with better absolute RMSE scores the CURSOR framework. This finding suggests that CURSOR's effectiveness is not solely dependent on having the best possible estimator in absolute performance.
RMSE vs. Pearson Correlation. Figure Sb illustrates the relationship between estimator perfor- mance ground truth data and the Pearson correlation statistic for the information retrieval (IR) task. The Best SVR model achieves lower RMSE than the standard SVR but demonstrates lower performance in ranking correlation with ground truth distances. The MLP performs nearly well as the Best SVR in the CURSOR framework despite a substantial difference RMSE. This counterin- tuitive result is possible due CURSOR's ' scoring function, which measures relative improvement. Further investigation into the mechanisms behind this phenomenon could yield valuable insights.
The relationship between estimator characteristics and CURSOR performance is complex, unlike typical supervised or unsupervised learning scenarios. These findings argue for ensemble methods that dynamically test different estimators within the CURSOR framework. Future research should focus on developing robust confidence metrics for CURSOR scores estimations.
Conclusions.
In conclusion, this study reveals the complex interplay between estimator characteristics and CUR- SOR performance. The framework's s ability to extract useful information from estimators with suboptimal absolute performance highlights its robustness and adaptability. These findings open avenues for future research, particularly in dynamic estimator selection strategies and improved confidence estimation techniques.
28

---

Medel Perforance: Scaring Time vs Commection
Shuffidd
BedNli
Bossvk
DurmySccringg Neen
Lanaa Regresioon
-0.4
8000 Shitte
1.2
6.9
19432
(b) Relationship between estimator performance and CURSOR effectiveness. The x-axis shows the RMSE of each estimator the target facc dataset (lower is better), while the y-axis shows the Pcarson correlation between the estimator's 's rankings and ground truth dis- tances (higher absolute value is better). Each point represents different estimator method, with error bars indicating standard deviation. Notably, methods with similar RMSE can have vastly different correlations, and some methods with higher RMSE (e.g.. MLP) can achieve correlations comparable to methods with lower RMSE (e.g.. BestSVR). This demonstrates that abso- lute estimator performance does necessarily predict effectiveness within the CURSOR framework.
(a) Comparison of estimator performance on aligned vs. shuffled EEG data. The x-axis shows the RMSE on aligned data, while the y-axis shows the RMSE on shuffled data, Each point represents a different estima- tor, with error bars indicating standard deviation. The diagonal dashed line represents equal performance both datasets. Points above this line indicate better per- formance on aligned data compared shuffled data, which is desirable for CURSOR. Note that while some methods (e.g., MIP) may have higher absolute RMSF, they still show improvement over the shuffled baseline, demonstrating potential utility in the CURSOR frame- work.
E.3
Ranking images and optimizing the image embedding
Images ranked and generated for the 17 targets are shown in Figure Table extends the main paper's results for EEG preprocessing using windowing (left) and includes a subset re-run using EEGNet embeddings (right). Trends and magnitudes are aligned between the two EEG representations, suggesting EEGNet embeddings as an alternative preprocessing method in the CURSOR framework. This allows us provide embeddings as attached data, overcoming the challenge of sharing raw EEG data.
EEG windowing
EEGNet
Regressor
top rank
R
Rank
top rank
R
Rank
LR
2.90 + 3.08
-0.77 + 0.04
6.63 + 5.75
2.66 + 3.02
-0.76 + 0.05
6,21 + 6.03
S-LR
20.85 13.48
-0.01 + 0.14
27.13 + 18.45
21.07 + 13.73
-0.01 0.13
26.35 + 16.93
Dummy
21.97 + 12.86
0.01
27.91 + 15.95
22.29 12.44
-0.01 + 0.14
27.73 + 16.60
MLP
12.11 11.07
0.11
21.68 + 15.48
13.26
11.46
-0.31 0.11
19.85 15.01
BestMLP
4.30 + 2.64
-0.81
9.67 + 10.02
3.91 = 3.53
-0.76 0.04
6.14 5.89
SVR
6.77
4.65
-.669
16.04 + 10.38
6.12
4.61
-0.69
0.06
15.65 12.85
BestSVR
12.60 + 5.53
-0.45 +: 0.11
43.34 + 13.37
13.17 + 4.48
-0.40 + 0.12
44.86 + 13.88
Table 2: Euclidean distance (d) at the top rank, coefficient of determination (R), and rank shown as mean + standard deviation for each estimator: linear regression (LR), shuffled linear regression (S-LR), dummy scoring mean, default and optimized ("best") multi-layer perceptron (MLP). and default and best support vector regression (SVR). Results are presented for two alternative EEG rep- resentations: one applying windowing over the raw signal, and the other using EEGNet embeddings.
29

---

Ranking
Optimization
Target
S-LR
Dummy
Target
LR
S-LR
Dummy
25
S
Figure 9: Images matching the top-scored hypothesis for each image target (left) and generated from the optimization (right) using Linear Regression (LR) and the control conditions Shuffle Linear Regression (S-LR) and Dummy Mean Scoring. Ideally, there would be no perceptible differences between LR and the target, while S-LR and Dummy would exhibit very high visual differences compared to the target.
30

---

E.4
Additional retrieval metrics
The Top-k and Min-d-at-Top-K metrics' tables, namely Table 3 and Table respectively, were not included in the main text as we considered the distance-at-top-raan (d-at-top rank) a more informative metric for our perceptual task, given that the rank does necessarily reflect perceptual similarity.
Table 3: Top-k Accuracies: Linear Regression (LR) vs. Theoretical Random Retrieval (60 Candidates)
Model
Top-1 Ace
Top-3 Ace
Top-5 Acc
Top-10 Ace
LR Random
0.171 + 0.029 0.016 + 0.010
0.353 0.037
0.565 + 0.038 0.081 + 0.021
0.800 0.031
0.049 = 0.016
0.155 + 0.028
Table 4: Minimum Distance to Target in Top-k Retrieved Hypotheses (Min-d-at-Top-k)
Model
Top-1 Min Dist
Top-3 Min Dist
Top-5 Min Dist
Top-10 Min Dist
LR 2.898 = 3.067 Random
1.010 + 1.312
0.524 + 0.861 7.688 + 6.498
0.176 + 0.450 4.194 + 3.828
23.064 + 13.316
11.532 + 8.933
In addition to the retrieval metrics presented above, we further investigated the stability and robustness of the CURSOR score with respect to random data splits and model variability. This analysis was motivated by reviewer feedback concerning the potential sensitivity of the CURSOR score computed as a relative RMSE ratio-to shuffling and data partitioning randomness.
As first step, we visualized the relationship between the predicted scores and the ground-truth perceptual distances across folds and random splits (Figure TOp. This plot shows the mean and standard deviation of each model's predictions function of the true perceptual distance, allowing us to inspect the uncertainty of scores directly. Notably, the MLP model displays visibly larger uncertainty ranges compared to other methods.
Scores (Mean Std) Distances by method
LinearRogression
LinearRRsgessionn
DummyScoring Mean
1.49
1.23
1.45
Weer 2203
Scone (PAN 5413
2.10
412
1.30
1.20
1.00
1.00
20
Trus Dolaanoo
Th. Obiaatoo
Trus Obiaaco
1.20
1.22
DeveeeMann 200
2.10
1.00
1.00
Figure 10: Relationship between mean + standard deviation of predicted scores and ground perceptual distances, aggregated over all runs. Each panel corresponds different estimator, illustrating how uncertainty varies across the perceptual space and highlighting the higher dispersion observed for the MLP model.
31

---

Across 10-fold crois-validation experiments, we confirmed those observations as the standard devia- tion of the CURSOR scores was approximately 2.5% of the mean for most methods N 9234, to the notable exception was the MLP model, which exhibited slightly higher variability at around 4% (Figure lTp.
scores_std _ea__ttetnntnttt_e_ mean vs Training Size (%)
method_ _nme
LinearRegression
Shuffle LinearRegression
DummyScoring Mean
SVR
Std as % of Mean
15
5
2000
4000
6000
8000
Training Size
Figure 11: Evolution of the standard deviation as percentage the mean CURSOR scores across increasing N for all evaluated methods. The plot shows exponential decay trend from approximately 20% to 2.5% for most models (4% for MLP), indicating improving score stability with increasing dataset size.
32

---

Finally, to quantify robustness more systematically, we computed signal-to comaal-to-niise noise ratio (SNR)-li like metric for the ranking task, defined as;
SNR = mp- std (ep.meanmmnmmmmmmmmmmmmmaa
mean www. (scores, axis=1)) np. mean (np.std std (scores, axis=1))
where scores are matrices of shape (60, 10) representing the ranking results across folds and random splits. This metric compares the signal variation across conditions to the typical uncertainty within them, and results are shown on Figure [2] Interestingly, for both S-LR and MLP, this ratio remained roughly constant around 0.4 for all N. In contrast, LR and SVR began similar values but increased approximately linearly with N, reaching 0.6 and 0.52, respectively, at = 9234.
Range Noise Ratio (# ste)
(=std)
0.7
0.6
I
0.5
Range Noise Ratio
0.4
I
E
0.3
0.2
Lincar Regression
Shuffie Linear Regression
Mean Dummy Regressor
SVR
0.1
0.0
2000
4000
Number of stimuli-response pairs observed (N)
Figure 12: Signal-to-noise -oisee ratio (SNR) as function of N for each model. LR and SVR show clear linear increase in SNR, reaching 0.6 and 0.52 at 9234, while S-LR and MLP remain approximately constant around 0.4 across all N.
When interpreted alongside the results in Figure this SNR analysis provides coherent explanation for the comparatively lower perceptual retrieval performance of MLP. Although MLP correlates with ground-truth distances, its higher cross-validation variance translates into lower effective SNR and hence less reliable top-rank predictions.
33

---

E.5
Computation time vs. performance
Figure 13 examines the relationship between computation time and performance. The primary
observation is the marked variability in computation time among methods. Given the weak correlation between performance and computation time, it may be advantageous to focus on faster-to-trail estimators when implementing CURSOR initially.
Model Performancc: Scoring Time vs Corelaaiinn
Pearson Corelation
-0.4
-02-
method name
BestMLP
0.0
DummyScoring, Kean
Lineerfegression
SVR
Shuffle_ inee Regression
30000
40000
Figure 13: Trade-off between computational time and performance in the CURSOR framework. The x-axis shows the scoring time in seconds, while the y-axis shows the Pearson correlation between the estimator's 's rankings and the ground truth distances. Each point represents different estimator, with error bars indicating standard deviation.
E.6 Optimization ablation and control
Our primary finding is that CURSOR recover target face within d < 5 even when stimuli within d < 10 have been observed. This demonstrates CURSOR's 's ability to infer beyond its current observation space, crucial feature for practical applications where near-target stimuli may be scarce. also suggests that non-logarithmic sampling scheme might be viable for future self-calibrating experiments.
Comparing the ablation and control conditions reveals the significant impact of near-target stimuli performance. When dremoved > 10, CURSOR unable to recover face at d from the target, even with 5432 stimuli-response pairs. In contrast, under uniform data reduction (control condition), similar performance was achievable with only N 2047 pairs.
This performance disparity at comparable values confirms that responses stimuli close the target significantly enhance CURSOR's 's accuracy. It suggests that strategic sampling, informed by CURSOR scores could potentially improve the efficiency of the algorithm practical scenarios and is worth further research. These results highlight CURSOR's robustness to data scarcity and its ability perform well even with limited near-target information, while also emphasizing the value of near- target stimuli for optimal performance.
34

---

E.7 Recovering ground -ruth labels.
Using the best stimuli h identified during the optimization process, we reconstructed labels for each observed response e, as the distance d - P% (3z). The quality of these reconstructed labels is measured by their RMSE compared to the ground-truth labels d= For LR. RMSE(d .d)) is 0.18 + 0.08, representing less than 0.4% of the range values and outperforming S-LR at 15.20 3.21 and Dummy at 18.23 +: 2.99 (both Mann- Mann-Whitney U, p < 0.001, Bonferroni corrected). It demonstrates our method's 's ability to recover ground-trus labels without supervision and suggests potential effectiveness in downstream tasks requiring labels comparable to calibrated approaches.
F Discussion, Limitations and Conclusions
E1 Limitations.
Online replication. Our findings should be replicated online settings using protocols that match simulations. This might surface additional constraints for practical deployment. For example, prolonged or repeated exposure might attenuate the P3 response, potentially affecting the system's 's reliability
Real faces. Our experiments were limited to synthetic faces and generalization to real existing faces yet to he explored and would constitute interesting future research. The core challenges would be: 1) new EEG experiments to acquire a novel dataset of stimuli-responses pairs, and 2) the introduction of new component to the algorithm, which allows a quantitative distance definition that correlates with real faces' visual similarity. This could involve encoding the faces in an embedding space of pre-trained model, or using predictions from pre-trained contrastive model comparing hypothetical targets to observed stimulus, among others.
Using more complex estimators. Although we have explored the combination of EEGNet em- beddings and MLP estimator, we did not directly used of full deep neural networkktransffrmer architecture, which might lead to better absolute prediction performance. However, as CURSOR computes relative score, the absolute performance of estimator on the ground truth data might not always be the most significant factor. Understanding this relationship would constitute interesting future research, which Appendix Figure 8a started to explore.
Optimization in reduced dimensional space. choice to conduct optimization a low-dimensional subspace (we run over 8000 experiments for our controls, shown in Appendix Figure 6). is considered standard to use dimensionality reduction 65).
While computational constraints motivated our
as part of a black-box box optimization procedures [1
Our decision to use 10D/20D is supported by Appendix Figure We ran the ranking task for different EEG dimensions (using PCA). At 20D, we reached near-asymptotic performance. Then, maintaining EEG at 20D, we reduced the image space (via PCA), and 10D maintained similar near-asymptotic performance. Performance was measured once images were converted back to their original 512D to ensure apples-to-apples comparison with ranking and human studies. In other words, used the smallest dimensionality that preserves task performance (20-D EEG, 10-D faces) consistent with assumptions in common frameworks [12
low-effective-
44
The limitation is not using dimensionality reduction per se, as all our metrics are reported after faces are projected back to their original 512D (using PCA inverse mapping) to ensure apples-to-apples comparison with ranking and human studies. Instead, because we relied on external metrics to choose 10D/20D, which would not be available in real scenario (i.e., correlation of scores vs grouhd-truth on the ranking task).
Future research could study performance using internal cameeniionality-rdddeeii metrics instead (e.g., reconstruction metrics). We did not test such latent space variants in this work and there may be alternatives that can improve over our results.
Active sampling. We have not explored any active sampling strategies and, for experiments, samples where draw uniformly without replacement. Active sampling [II] might help reduce the
35

---

amount of EEG-stimuli pairs required to identify a target, which is currently a significant limitation practical use.
Meta Self-Calibration In its current form, our self-calibration algorithm does not learn any "persistent" parameters (that are estimated and then frozen for later use), apart from those of its estimators f(Ox) [[Alg. [ line B) and (Alg. 6)]. It would be interesting research to generalize CURSOR estimator for a specific task/domain based on large unlabeled dataset so that (T, h)) + scores for any I' and h. In this scenario, 0 would constitute learned "persistent" parameters for generalized CURSOR estimator.
F.2 Ethical implications and potential applications.
Ethical considerations are crucial in this line of research as the ability to infer people's intent without explicit questioning or consent raises important privacy and ethical concerns that warrant caution of application scenarios [14] 177. Potential negative societal impacts, due to performance instability in risk-sensitive applications, need to carefully addressed. Working on explainable SC-BCIs and methods to assess confidence of their predictions will play a crucial role.
Physiological data can be sensitive and re-identifiable, and consent mechanisms often opaque in practice, which increases the importance of cognitive privacy aspects for BCI research [66 52 39 14).
The core risk is that models or datasets are repurposed beyond the stated task to infer mental content without informed, ongoing consent. For that, we advocate for:
Acknowledging risk explicitly: We will state that inferring internal mental content without user intent unacceptable and a central ethical concern [10, 64).
Requiring opt-in, revocable consent: Participation must be explicit, comprehensible, and withdrawable, with data erasure upon request [6] 49).
Imposing purpose limitation and governance: Models and data may only be used for the approved task and context, with consent required for secondary and audits
recorded 53 54 (1).
Implementing data minimization and secure handling: Prefer on-device or sandboxed processing; encrypt data in transit/at rest; set short retention windows; significantly limit the sharing of raw EEG (7).
Increasing transparency and accountability: Release model cards and datasheets document- ing intended use, limits, failure modes, and oversight plans [48 18).
Centering on user agency: Provide a visible "record/mute" control, require explicit start/stop for any recording, and disable inference outside the study context by default (14).
Furthermore, our study is offline, task-hound, and conducted with consenting volunteers; do not perform real-time or covert inference, and outputs are analyzed in aggregate, which serve as mitigations.
Despite potential risks, this technology could have beneficial applications, particularly in diagnostic scenarios where user feedback is unreliable or calibration is difficult, such early diagnosis of neurodegenerative disorders [58]. Future research should explore such positive use cases while remaining vigilant about potential misuse.
36


Reviews:
[{"review_id": "LuADRy0MVL", "reviewer": "Reviewer_m8FY", "summary": "The paper presents an approach to recover the image a participant holds in their mind given their EEG brain data in response to multiple images that differ more or less from the target image. This approach does not require labelled data and relies on previous research showing that distance between visual concepts can be predicted from the EEG. The authors collected a new EEG dataset on 31 participants, in which images of faces generated with a GAN (previously trained for this purpose) are presented in an RSVP oddball paradigm. The proposed algorithm, CURSOR, is then used to predict the distance (in latent space) between each shown image and a series of hypothesis images, given EEG responses. Results suggest that the version of the algorithm based on linear regressors enables retrieving or reconstructing the target image, or a perceptually indistinguishable variant, as opposed to shuffled/dummy baselines.", "questions": "1. I don’t understand how CURSOR can be evaluated on all subjects and trials at once. From what I understand, the score depends on how well the distance between a hypothesis image and a presented image can be modelled from EEG. If this distance is well modelled (i.e., high RMSE ratio), then the hypothesis image is a good candidate. However, since the target image differs from trial to trial, this process must be run for each trial individually. In this case, what does it mean to scale the dataset size beyond the number of related images available in a trial (e.g. in Figure 3)? I believe I must be missing something obvious here.\n2. What is the trajectory the related images are sampled from (line 157), i.e., is another point in latent space randomly sampled and used to provide the direction?\n3. This is purely a suggestion to help clarify how CURSOR differs from a supervised approach (conceptually, for the reader, but also in terms of performance) where labels are available. Would it be possible to integrate one such approach as a baseline? For instance, by training a predictor in leave-one-subject-out (or leave-one-trial-out) fashion that learns on ($e_i$, $z^*$) pairs, and comparing its ranking/predictions to the model.\n\nSmall comments\n\n* I assume that each channel is averaged across time within each time window, however it is not specified in the text, e.g. line 167.\n* Appendix B says there are 16 target images, but Section 5 (l156) says there are 17.", "limitations": "Yes.", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\n* Quality: The submission appears technically sound, with claims supported by experimental results. For instance, the alignment between the CURSOR score and human judgment of distances is first validated, as well as the distance threshold for perceptually similar images (Figure 2). The comparison between the proposed linear regression-based model and controls (a version with a dummy regressor or a version trained on a shuffled dataset) supports the feasibility and effectiveness of the approach. Finally, the examples shown in Figures 4 and 10 overall support the quantitative results.\n* Significance: I am not familiar enough with the specific problem of mental visual target recovery to evaluate the impact this approach may have on the community. However, given the new unsupervised paradigm, and unique accompanying dataset, this submission is likely to help design further studies on this topic.\n* Originality: the submission’s approach to the recovery of mental visual targets appears to be novel, as is the data that has been collected as part of the study. Relevant previous work is cited, and the difference with existing paradigms is described (see Q3 for a suggestion to further clarify this).\n\nWeaknesses\n\n* Clarity: I found the manuscript overall difficult to navigate, though this may be due to me being less familiar with the topic. A key point that I think should be clarified is how CURSOR can be evaluated on all subjects and trials at once, i.e. what a N=9234 actually means (see Q1). Also, it was not obvious from the main text and appendix that the total number of examples (9234) was obtained by only keeping the 28.5% of oddball images from the 1120 images shown in a trial.", "was_revised": true, "final_justification": "The authors have addressed my concerns and clarified the functioning of their proposed approach during the discussion period. Their experimental results overall seem to support the claims, and their proposed label-free approach is novel. With this in mind I raise my score to an accept, and suggest to the authors that they include the content of their clarification answers in the manuscript.", "rebuttal": "We appreciate Reviewer m8FY’s rigorous critique which directly contributed to improving the clarity of our revision.\n\n## Q1: How can CURSOR be evaluated on all subjects and trials at once? + dataset scaling clarification\n\nThe neurophysiological data were indeed acquired in controlled conditions during which we collected triplets of $(z_i,e_i, z_i^\\*)$, where $z^\\*$ is the target we asked the user to keep in mind during each experiment. This is explained in Section 5 L168: \"Our final dataset consists of 9234 stimuli-response pairs, with each entry composed of a $z^\\*_i$ vector in R512, a stimuli $z_i$ vector in R512, and an EEG response vector $e_i$ in R203\". Seventeen different targets were used during our neurophysiological data acquisition, and the Reviewer is correct that, as is, we could not use the entire dataset for our experiments.\n\n**How we “scaled the dataset size beyond the number of related images available”**\nWe needed $(z_i,e_i)$ pairs all associated with the same target to have enough data. Given previous work confirmed that $e_i$ encodes $\\lVert  z^\\* - z_i \\rVert_2$  [delatorreortiz2023], we could generate arbitrary new datasets $\\psi_{N}$ containing N=9234 $(z_i,e_i)$ pairs where the EEG responses remained associated with the original target-stimuli distance (i.e., preserving the original statistical properties).\n\n[delatorreortiz2023] The P3 indexes the distance between perceived and target image\n\nThis is explained in Section 6.1: “Not all stimuli-response pairs in the dataset were collected with the same target face $z^\\*$. To enable our study, we generated equivalent datasets, each with a single target face $z^\\*$, by generating new stimuli in the latent Z-space that preserved the original statistical properties. We used gradient descent, starting from a randomly sampled 512-dimensional starting point, to match the Euclidean distance from the target in the original dataset to the new target. We created 10 dataset variants for each of the 17 target faces utilized in our neurophysiological experiments, yielding 170 stimuli-response datasets that preserved the original statistical properties”.\n\n**Implication:** We are mixing EEG data from different participants and use them as if coming from a unique participant, which might make the self-calibration harder to solve. A significantly larger dataset attached to a single participant would need to be collected and tested to evaluate such claims.\n\n**Changes to the paper.** We understand our text could be confusing and will update the opening sentence of Section 6.1 to make it more concrete.\n\n\n## Q2: What is the trajectory the related images are sampled from?\n\nTrajectories move “diagonally” in the 512D-dimensional latent space, i.e. equally far in every coordinate direction. We simply add a small positive value to each component of the source image at monotonically increasing $d$ values with logarithmic spacing (denser near 0). We will clarify at L157 directly.\n\nFor context, this is standard practice in face research, with the rationale that moving along one latent direction yields small, smooth, and monotonic changes in facial appearance, reducing uncontrolled feature leaps [valentine1991,rotshtein2005,kahn2010,gratton2013]. Adhering to such established best practices decreases risk in the data acquisition experiment, promoting data quality.\n\t\n- [valentine1991] A unified account of the effects of distinctiveness, inversion, and race in face recognition\n- [kahn2010] Temporally distinct neural coding of perceptual similarity and prototype bias\n- [rotshtein2005] Morphing Marilyn into Maggie dissociates physical and identity face representations in the brain\n- [gratton2013] Attention Selectively Modifies the Representation of Individual Faces in the Human Brain\n\n## Q3: Would it be possible to integrate one supervised approach as a baseline? For instance, by training a predictor in leave-one-subject-out (or leave-one-trial-out) fashion that learns on (e_i, z*) pairs, and comparing its ranking/predictions to the model.\n\nThank you for the suggestion. We discussed this internally prior to paper submission and decided against this direction as we were concerned that an emphasis on absolute performance might overshadow the contribution of our self-calibrating (label-free) approach.\n\nTo avoid this issue while still allowing a fair comparison, we decided to report the reconstruction error of the ground truth target (Section 7 “Recovering ground-truth labels” and Appendix F6) as a proxy to calibration performance for any possible downstream tasks and methods. The rationale is that if self-calibration allows for accurately reconstructing the ground-truth labels, then any downstream supervised methods can be applied with their own specific requirements, constraints, etc.\n\nThe reconstruction of labels can be done using the target identified via self-calibration as $\\hat{d} = ||\\hat{z}, z||_2$. Our label‑free CURSOR pipeline reconstructs these distances with RMSE = 0.18 $\\pm$ 0.08 (Appendix F6). For context, a supervised decoder on the same EEG task and featurization (29 channels x 7 time windows, 203 features) has been reported in [delatorreortiz2024] with RMSE = 0.17 $\\pm$ 0.09 on their aggregated evaluation. We will add this side‑by‑side comparison in the Appendix and note that cross‑validation meta‑parameters differ slightly across papers.\n\n[delatorreortiz2024] Perceptual Visual Similarity from EEG: Prediction and Image Generation\n\n\n## Small Comments (SC)\n\n**SC 1: I assume that each channel is averaged across time within each time window, however it is not specified in the text, e.g. line 167.**\n\nYou are correct, within each of the 7 windows, we average the samples across time for each channel (after baseline correction), producing one scalar per (channel, window); stacking across 29 channels and 7 windows gives the 203‑D vector used by the estimators. We will make this explicit in Section 5.\n\n**SC 2: Appendix B says there are 16 target images, but Section 5 (l156) says there are 17.**\n\nIt is 17 indeed. Thank you, we will fix the typo."}, {"review_id": "qdgKjJEswy", "reviewer": "Reviewer_VJPj", "summary": "This paper proposes a self-calibrating BCI to retrieve face images from raw EEG responses projected to a lower-dimensional space. Instead of training the system for classification using labelled data, which is customary, it exploits an estimator to predict distances to potential targets in the EEG domain, with the estimator trained using distances between face images in an image latent space. An error function is used as a measure of predictability to locate a target image, an image that a human participant has first looked at and then been asked to remember, while a batch of images is quickly shown to the participant, and EEG responses are recorded.", "questions": "What does it mean that the proposed algorithm is self-calibrating? In what sense are other algorithms calibrated? Calibration typically means that you determine a set of parameters using a set of known examples, and then keep these parameters fixed in further experiments. What would those parameters be here?\n \nWhy would it be a novelty to work in continuous domains, and what does this mean in this context? The only relevant domains are the image domain, the latent image domain, and the EEG domain. Which of these are typically not continuous?\n\nHow is the iterative search being done? Iterative over what?", "limitations": "yes", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 1, "originality": 3, "strengths_and_weaknesses": "This paper introduces the first self-calibrating BCI for continuous domains, which is shown in experiments to work well for mental target retrieval using face images as targets. A dataset of as many as 9234 image-EEG pairs collected from 29 subjects is also presented. A user study is also being conducted, from which a strong correlation between the proposed scores and perceived similarities could be observed. \n\nIt is mentioned seven times on the first two pages that the proposed framework is the first self-calibrating BCI for continuous domains. The question is whether this is indeed the case. The problem formulation and assumptions in this paper seem to be very similar to those in [1], which contains a system for iteratively searching for a target face image by gradually locating a subregion in the image latent space using sequences of EEG responses. This paper does not assume labeled training examples either, which is true for other earlier examples referred to in the same paper.\n\nThe proposed error function, with its associated score function, measures how well an estimator can be trained to predict distances to a given target from EEG, using a set of paired images and EEG responses. The assumption is that if the error is small, the target image is likely to be correct, but if the target is incorrect, the error ought to be larger. From the experimental results, this seems to be a valid assumption, at least for an estimator based on linear regression, but not at all for one based on a multi-layer perceptron. The problem with this argument is that it will never work with more competent estimators. \n\nModern neural networks can be trained to predict any function, even if the distances in the training data are completely random, as long as the amount of data is limited, which is true in this case. For such a network, you are thus likely to get almost no errors at all. That is probably why the linear regressor seems to be preferred in this case. It could be that the distance function becomes more linear when the target is correct, not that the error becomes smaller and the predictability higher.\n\nIt is claimed that the proposed method includes an iterative search for a target, but it is not described how this search is done. Section 4.3 is vague indeed and includes few details. There is nothing that excludes the possibility that the actual search is indeed brute force through either all examples or a reduced set of examples. \n\nUnfortunately, there seem to be no experiments on any real face images. The retrieved images are almost exactly the same as the target one, if everything works well, but this is highly unrealistic in a real context, when you use anything beyond a limited set of generated images.\n\n[1] Rajabi et al., “Mental Face Image Retrieval Based on a Closed-Loop Brain-Computer Interface”, HCII, 2023.", "was_revised": true, "final_justification": "Given the additional information provided in the rebuttal, which would have been nice to have seen in the actual paper, there is no reason to change the recommendation.", "rebuttal": "We thank Reviewer VJPj, especially for the questions on the framing of self‑calibration, which prompted productive discussions among the authors. We will clarify misunderstandings, notably regarding Rajabi et al. [1], which includes a *supervised, per‑subject classifier‑training phase*.\n\n## Differences with Rajabi et al [1]\n\nWe disagree that [1] “does not assume labeled training examples either”. In [1] section 4.2 says “the classifier is trained for each subject separately using their EEG data collected while performing a target face detection task (Sect. 5.2)”, and, in section 5.2, it is clear that the process is a “two-phase experiment: first, we train the EEG classifier for each subject (the classifier task) and subsequently we evaluate the performance of our closed-loop system in a mental face image retrieval task (the closed-loop task)”.\n\nReusing the same elements of language as in [1], our paper introduces a method that can start tackling the “closed-loop task” directly, without requiring the prior “classifier task”. Specifically, [1] explicitly includes a supervised, per‑subject classifier task trained on labeled EEG before the closed‑loop retrieval phase. Our method addresses the closed‑loop task directly and is label‑free/self‑calibrating. This is the core contribution of our paper and is, to the best of our knowledge, novel.\n\n\n## Q1: What does it mean that the proposed algorithm is self-calibrating? In what sense are other algorithms calibrated? Calibration typically means that you determine a set of parameters using a set of known examples, and then keep these parameters fixed in further experiments. What would those parameters be here?\n\n**What is self-calibrating, and how is CURSOR self-calibrating?** We say CURSOR is self-calibrating because we do not need an explicit calibration phase to get labels. \n\n---\n\nIn a **calibrated** approach, typical stages are; \n\nPhase 1: Calibration:\n\n1. collect EEG responses to images of faces, while the participant keeps a specific face in mind (the target face is **known to the experimenter**). Here we collect (eeg, z) pairs and know $z^*$.\n2. compute labels based on the distance to the known target face ($d = ||z^*, z||_2$),\n3. train a decoder $f_\\theta(eeg) \\mapsto d$ and freeze its parameters $\\theta$,\n\nPhase 2: Downstream task:\n\n4. use the frozen decoder for inference on downstream tasks\n\n---\n\nWith **self-calibration**; \n\nPhase 1: Self-calibration:\n\n1. collect EEG responses to images of faces, while the participant keeps a specific face in mind (the target face is **unknown to the experimenter**). Here we collect (eeg, z) pairs, but **we do not know $z^*$**.  Then, recover the target face via CURSOR. Here we estimate $\\hat{z}$ as our best guess for z*. This is the core contribution of the paper. Our results support that $\\hat{z}$ is close to z* (Figure 3 - right and Figure 4 - left).\n\n2. compute labels based on the distance to the estimated target face ($d = ||\\hat{z}, z||_2$). Our result (Section 7: Recovering ground-truth labels) shows we can recover ground-truth labels with minimal error. **Our paper stops here, as once labels are recovered, it is technically trivial to follow with**:\n3. train a decoder $f_\\theta(eeg) \\mapsto d$ and freeze its parameters $\\theta$,\n\nPhase 2: Downstream task:\n\n- 4. use the frozen decoder for inference on downstream tasks\n\n---\n\n**What parameters are being estimated?** With the above in mind, CURSOR itself does not have ”persistent” parameters that are being estimated and then frozen for later use. It would be interesting research to generalise a CURSOR estimator for a specific task/domain based on a large unlabelled dataset so that $f_{\\theta}(\\Gamma, h) \\mapsto scores$ for any $\\Gamma$ and $h$. $\\theta$ would constitute “persistent” parameters for a generalized CURSOR estimator.\n\n\n**Manuscript changes.** We will explicitly refine the definition of *self‑calibrating* in the Introduction to address the above.\n\n## Q2: Why would it be a novelty to work in continuous domains, and what does this mean in this context? The only relevant domains are the image domain, the latent image domain, and the EEG domain. Which of these are typically not continuous?\n\nIt is novel for self-calibrating systems, not for BCI tasks. We discuss this in L29-L32, and it is detailed in the related work section.\n\nPrior self‑calibrating pipelines operated on finite, discrete stimulus sets (e.g., letters, colors, flashes) with binary feedback from EEG (match/non‑match), and framed calibration as a classification problem. By contrast, our method calibrates without labels over a continuous latent stimulus space (hypotheses $h \\in \\mathbb{R}^{512}$) using a continuous‑valued feedback as the latent‑space distance $\\rho_h(z_i)=\\lVert h-z_i\\rVert_2$.\n\n**What “continuous” means here:**\n- **Stimuli:** We search a continuous latent stimulus space rather than a finite list of categories.\n- **Feedback information:** EEG encodes real‑valued distances $\\rho_h(z_i)$, not binary labels.\n\n**Consequence for algorithm design (why this is non‑trivial).** Continuous stimulus space and continuous‑valued feedback require (i) regression instead of classification and (ii) hypothesis‑centric search over an effectively infinite set.\n\n**Positioning and claim.** To our knowledge, this is the first label‑free, self‑calibrating BCI that performs closed‑loop search over a continuous latent stimulus space using a continuous‑valued feedback signal. We will revise the Introduction to state the claim at this level of precision.\n\n\n## Q3: How is the iterative search being done? Iterative over what?\n\nIterative search is the framework we use to position our work. \n\nIn the iterative search framework, scores are computed iteratively as new EEG-Image pairs are collected **while interacting with a user**. This is why we report most results with N (we iterate over N) as the x-axis, showing how performance changes as we iteratively acquire more data.\n\nReferring to the wording used in [1], we call “iterative search”, what [1] calls a “closed-loop task”, with the key difference that our evaluation is **offline** (iterative but non‑interactive) with EEG recorded during a **pre‑generated** sequence, and all iterative analyses are performed post‑hoc.  We also do not perform any **active sampling** in our EEG and computational experiments, with the following samples selected randomly from the remaining data. This differs from typical “closed-loop” systems.\n\nWe will ensure the difference with “closed-loop” systems is clear in our related work section in the paragraph starting L74 and cite [1] as a supporting example. \n\n\n## Using CURSOR with more complex estimators\n\n- “it will never work with more competent estimators.”\n- “Modern neural networks can be trained to predict any function, even if the distances in the training data are completely random, as long as the amount of data is limited”\n\nWe kindly disagree with these statements. Potential overfitting issues are accounted for by (1) using cross-validation to compute scores, ensuring we do not train and test on the same data, and (2) using relative scores against a shuffled control (i.e., completely random training data), ensuring overfitting or poor-performing estimators are neutralized.\n\n**Cross-validation.** With proper cross‑validation, any improvement that generalizes to held‑out data typically reduces the aligned error more than the shuffled error, increasing $S(h)$ for hypotheses consistent with the true target while leaving inconsistent hypotheses near the null $S(h)\\approx 1$. Hence, a stronger estimator does not break CURSOR.  Empirically, **we already include a stronger/nonlinear model** variant (e.g., EEGNet feature extractor + MLP head in Appendix Table 2) and observe that performance is not degraded.\n\n**Relative scores.** CURSOR never ranks hypotheses using raw predictions alone; it uses a **relative error improvement** computed on test splits via cross-validation. If a model performs similarly on aligned than on shuffled labels (due to poor performance or overfitting), then $S(h)\\approx 1$ for all $h$, which could be detected and used to prevent an early decision.\n\n**Manuscript changes.** We will further emphasize that aligned and shuffled RMSEs are computed on identical held‑out folds to clear up any confusion about overfitting and leakage.\n\n\n## No experiments with real faces\n\n**What we did (scope and limitation).** We would appreciate it if the Reviewer could clarify what they meant by “no experiments on any real face images”. Our EEG study used GAN‑generated, photorealistic face stimuli as proxies for photographs. We acknowledge that we did **not** collect EEG responses to photographs of real identities, and we will state this limitation in the revision, as we already discussed generator/latent‑space constraints in the paper.\n\n**Generate vs. retrieve (why near‑duplicates can appear).** Our method does not retrieve from a fixed gallery; it selects a latent hypothesis and then generates the image via the pretrained generator. When the selected hypothesis is very close to the target in latent space, the synthesized output can be perceptually indistinguishable from the target image (as per our H-ID experiment, Figure 2 - right), yet it is not an exact copy, confirmed by subtle differences, e.g., in background details.\n\nImages generated under Linear Regression (LR) remain perceptually close to the target, whereas baseline methods (S‑LR, Dummy) that participants in H-ID could identify as different from the target. This qualitative pattern is consistent with the quantitative H‑ID metrics in Figure 2. We will ensure Section 7 reports these observations more explicitly."}, {"review_id": "oHS1A8n4Js", "reviewer": "Reviewer_c1xC", "summary": "This work introduces a self-calibrating framework that recovers mental targets (e.g., imagined faces) from EEG and image data without using labeled data or pre-trained models. In particular, the framework generates a hypothetical mental target as an embedding of a face the user might be imagining and constructs a dataset that relates observed faces to their distances from this target. It then trains an estimator to predict these distances from EEG data and compares its performance to that of an estimator trained on a mismatched dataset. The resulting score reflects how well the hypothesis matches the true mental target, with the score expected to peak when the hypothesis aligns with the actual target the user had in mind. Through this mechanism, one can predict perceptual similarity, rank images relative to an unknown target, and generate target-like images. The authors also release a new EEG dataset, face stimuli, and codebase to support future research.", "questions": "1. Did you experiment with non-linear estimators? I have my doubts a linear estimator is the best choice for this problem.\n2. For the dummy condition, in L239 the authors say “average of all distances in Gamma set”. Is this average computed in the training split or all the samples?\n3. I miss results with at least one supervised method from prior work. Can you include some? I understand the premise here is to avoid labeled data but it would be good to show the gap between the presented approach and the current label-based methods in terms of accuracy. \n4. In table 1, I would like to see top-k accuracy as well to have a better intuition of the performance. The distance metric is somewhat hard to use for comparison. Accuracy would also be needed if item 3 is included.", "limitations": "Yes", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "A major strength of this work is its introduction of a label-free method for mapping EEG to image data through self-calibration, overcoming the limitations of prior approaches that depend on labeled EEG-image pairs. Another key contribution is the release of a paired EEG and image dataset, which can support and encourage further research in this area. \n\nAlthough I’m not deeply familiar with standard practices in BCI, I found it particularly clever that the authors operate in the latent space of images—this enables manipulation of ground-truth distances prior to the generation of observable data. \n\nAs acknowledged by the authors, a primary limitation of the approach lies in the search procedure: the framework restricts the hypothesis space to a predefined set that includes the true mental target. This makes the framework effective as a discriminator, but as a classifier, it requires manual curation and prior knowledge to ensure the target is within the search set. I also raise some concerns about the evaluation, which I elaborate on in the Questions section below.", "was_revised": true, "final_justification": "The authors have addressed my questions and added additional results that reinforce the initial conclusions. Furthermore, in my opinion, they also adequately addressed other reviewers' concerns.", "rebuttal": "We are grateful to Reviewer c1xC for a thorough review and for praising the novelty and contribution of our work. Following their recommendations, we have significantly improved our manuscript by rerunning experiments that yield additional insights on CURSOR’s performance (top-k accuracy).\n\n## Q1: Did you experiment with non-linear estimators? I have my doubts that a linear estimator is the best choice for this problem.\n\nYes. In addition to linear regression (LR), we evaluated a multilayer perceptron (MLP) and support vector regression (SVR) with an RBF kernel under the same training/validation protocol (L224). Results for all estimators are summarized in Table 1, with a detailed breakdown in Appendix Figure 8. We also tested a non‑linear EEG representation using EEGNet embeddings as inputs; results are reported in Appendix Table 2. Across these settings, linear regression achieved the best performance.\n\nPlease also see our comment to Reviewer VJPj (\"Using CURSOR with more complex estimators\") regarding the estimator's absolute performance. As CURSOR computes a relative score, the absolute performance of an estimator on the ground truth data might not always be the most significant factor. Understanding this relationship would constitute interesting future research, which Appendix Figure 8 started to explore.\n\n## Q2: For the dummy condition, in L239 the authors say “average of all distances in Gamma set”. Is this average computed in the training split or all the samples?\n\nThe training split. We keep everything the same (i.e., the cross-validation procedure is the same) and simply swap the estimator for the dummy estimator. The estimators thus only see the training set as for all other conditions. We further emphasize that aligned and shuffled RMSEs are computed on identical held‑out folds; thus, the dummy estimator will always give the same prediction for the aligned and shuffled sets, leading to a CURSOR score of 1. This is the most basic baseline possible to verify that no bias or information leaks exist in our pipeline.\n\n## Q3: I miss results with at least one supervised method from prior work. Can you include some? I understand the premise here is to avoid labeled data but it would be good to show the gap between the presented approach and the current label-based methods in terms of accuracy.\n\nThank you for sharing this concern. We kindly refer this Reviewer to our answer to Q3 from Reviewer m8FY.\n\n## Q4: In table 1, I would like to see top-k accuracy as well to have a better intuition of the performance. The distance metric is somewhat hard to use for comparison. Accuracy would also be needed if item 3 is included.\n\nWe re-ran experiments to compute top-k accuracies for the LR method and compare with the theoretical top-k for random retrieval (out of 60 candidates). Please see the table below:\n\n| Model                       | Top-1 Acc      | Top-3 Acc      | Top-5 Acc      | Top-10 Acc     |\n|----------------------------|-------------------|----------------|----------------|----------------|\n| LinearRegression           | 0.171 ± 0.029  | 0.353 ± 0.037  | 0.565 ± 0.038  | 0.800 ± 0.031  |\n| Theoretical Random Retrieval | 0.016 ± 0.01 | 0.049 ± 0.016  | 0.081 ± 0.021  | 0.155 ± 0.028  |\n\n**With respect to Q3 (“item 3”)**, our hypothesis set does not have an EEG attached to them, and we thus could not rank them based on a pre-trained decoder. A significant strength of CURSOR is that it can estimate the score of any hypothesis face $h$ based on a separate dataset of (eeg, z) pairs. A decoder-based scorer, trained on (eeg, d) pairs, would need access to one (eeg, h) pair to be able to score that specific $h$.\n\n**Why do we report d-at-top-rank and not top-k?** We chose to report the d-at-top rank in our initial submission (Figure 3 and Table 1) because we think it is more informative for our perceptual task. If the top-1 face is not the correct target but is visually similar to the target (i.e., its distance to the target is small, see H-ID experimental results Figure 2 - right), we would argue that reporting that distance is more informative than whether it is the correct answer. In addition, our hypothesis distance to the target was sampled uniformly (L185); thus, the rank, while a very good proxy, does not necessarily reflect perceptual similarity.\n\nWe will add a table for min-d-at-top-k in the Appendix. This is the minimum distance to the target in the top-k retrieved hypothesis, informing us whether the top-k retrieved set contains an image perceptually identical to the target when combined with the H-ID results.\n\nThe table below compares the LR method with the theoretical top-k for random retrieval (out of 60 candidates):\n\n| Model                       | Top-1 Min Dist  | Top-3 Min Dist  | Top-5 Min Dist  | Top-10 Min Dist |\n|----------------------------|-----------------|------------------|------------------|------------------|\n| LinearRegression                         | 2.898 ± 3.067   | 1.010 ± 1.312    | 0.524 ± 0.861    | 0.176 ± 0.450    |\n| Theoretical Random Retrieval | 23.064 ± 13.316 | 11.532 ± 8.933   | 7.688 ± 6.498    | 4.194 ± 3.828    |"}, {"review_id": "E9RPTK4RZf", "reviewer": "Reviewer_Dn3x", "summary": "This paper presents a self-calibrating brain-computer interface approach to identify and reconstruct a mental image target (a face that the user is thinking of) using only unlabeled EEG signals and stimuli embeddings. Unlike prior approaches that rely on labeled data or pre-trained decoders, CURSOR estimates target similarity scores in a continuous latent space derived from a generative model, without requiring any supervision. The method defines a score function based on how well a regressor trained on EEG-to-distance pairs generalises compared to a shuffled baseline. Using this score, CURSOR ranks and optimises hypothesised mental targets in latent space. The authors also describe a large EEG dataset collected under controlled conditions and they validate their approach through correlation with human perceptual judgments, control experiments, ablations and benchmarks.", "questions": "Can you justify the need for optimisation in reduced-dimensional spaces beyond computational cost? Does optimisation in 10D/20D reliably reflect performance in the original 512D/203D latent spaces? \n\nLine 159 (logarithmic sampling) and line 161 (“bias mitigation”) lack citations or clarification. Can you point to specific justifications and follow-ups?\n\nHow stable is the CURSOR score across different random splits for shuffled baselines? Since the score is computed via a relative RMSE ratio, do you observe high variance depending on the shuffling?", "limitations": "CURSOR enables mental target reconstruction using EEG, which raises important ethical concerns around cognitive privacy and unintended inference. The possibility of inferring internal mental content without explicit user intention should be acknowledged. A brief discussion of these risks and possible safeguards (e.g., user consent, secure model deployment, opt-in constraints) would strengthen the paper's ethical framing.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n- Proposes the first self-calibrating BCI that operates in continuous latent spaces (e.g., 512D GAN embeddings) instead of discrete labels. Enables mental target recovery without calibration, labels, or pre-trained decoders.\n\n- Introduces a relative RMSE-based score comparing aligned and shuffled EEG-stimulus datasets. This breaks the circular dependency between unknown distances and regressors. An interesting approach.\n\n- Demonstrates high alignment with human perception, accurate ranking and successful optimisation to sub-perceptual distances.\n\n- An EEG dataset with continuous latent-space stimuli and task-specific engagement. \n\nWeaknesses:\n\n- The introduction is not really clear and fails to explain the core problem and contributions of the paper. While it gestures at mental target recovery and self-calibration, it does not clearly explain how CURSOR works or what makes the problem novel. Terms like \"continuous domains\" and \"mental targets\" are used without grounding the reader in how EEG signals and image embeddings are being connected. Key elements, like the role of the latent space, the unsupervised regression formulation or the scoring mechanism, are absent or vague. As a result, the actual technical framing only becomes understandable much later in the method section, which weakens accessibility for readers who are not experts.\n\n- The abbreviation CURSOR (Consistency-based Unsupervised Regression for Similarity-based Optimization and Ranking) feels contrived and only loosely tied to the actual method. While the method does involve unsupervised regression and ranking based on similarity scores, the notion of \"consistency\" is not a clearly defined or central mechanism in the algorithm. There is no consistency regularisation or self-distillation and there is only a relative RMSE comparison between aligned and shuffled EEG–stimulus pairs. Additionally, optimisation and ranking refer to downstream uses of the score, not core components of the algorithm itself.\n\n- Line 159 claims that \"This logarithmic sampling aligns with neuroscientific methodologies for studying perceptual thresholds and neural adaptation\". This needs to be cited.\n\n- Line 161 mentions “mitigations for these biases” but neither defines the biases nor explains the mitigations later. There is no clear follow-up. This statement is unsubstantiated and should be clarified or removed.\n\n- The justification for the two-stage evaluation, ranking in full space and optimisation in reduced-dimensional space, feels weak and underexplained. The authors cite computational challenges as the reason, but provide no concrete analysis of resource constraints or runtimes. More importantly, reducing the dimensionality (from 512D and 203D to 10D and 20D) changes the problem structure and may artificially boost optimisation performance. This shift is not examined and the paper treats it as a benign workaround rather than a methodological compromise.\n\n- The paper is difficult to read and hard to follow.", "was_revised": true, "final_justification": "The paper provides an interesting contribution. The authors have addressed many of my and other reviewers' concerns. If the point of clarity are addressed in the final version of the paper, this can be accepted.", "rebuttal": "We appreciate Reviewer Dn3x’s comprehensive and constructive commentary, which allowed us to deepen our analysis (“stability across different random splits”) and expand our ethics discussion.\n\n## Q1: Can you justify the need for optimisation in reduced-dimensional spaces beyond computational cost? Does optimisation in 10D/20D reliably reflect performance in the original 512D/203D latent spaces?\n\n**Why report optimization in a low‑dimensional subspace?** While computational constraints motivated our choice (we had to run more than 8000 optimization experiments for our controls in Appendix Figure 6), it is considered standard to use dimensionality reduction as part of a black‑box optimization procedures [constantine2014,letham2020,harkonen2020,shen2021].\n\n**Why 10D/20D?** Our decision to use 10D/20D is fully supported by Appendix Figure 5. We ran the ranking task for different EEG dimensions (using PCA). At 20D, we observed the same performance. Then, maintaining EEG at 20D, we reduced the image space (via PCA), and 10D maintained similar performance. Performance was measured once images were converted back to their original 512D to ensure apples‑to‑apples comparison with ranking and human studies. In other words, we used the smallest dimensionality that preserves task performance (20‑D EEG, 10‑D faces) consistent with low‑effective‑dimension assumptions in common frameworks [constantine2014,eriksson2020,letham2020]. We will clarify this in the Appendix section C.\n\n**Is this a limitation?** We acknowledge this as a limitation in L328, and to our knowledge, it is not possible to guarantee the optimization performance directly on the full 512D search space based on our reduced-dimensional results. The limitation is not in using dimensionality reduction per se, as all our metrics are reported after faces are projected back to their original 512D (using PCA inverse mapping) to ensure apples‑to‑apples comparison with ranking and human studies. Instead, it is because we relied on external metrics to choose 10D/20D, which would not be available in a real scenario (i.e., correlation of scores vs ground-truth on the ranking task). Future research could study performance using internal dimensionality reduction metrics instead (e.g., reconstruction metrics).\n\n- [constantine2014] Active Subspace Methods in Theory and Practice: Applications to Kriging Surfaces\n- [harkonen2020] GANSpace: Discovering Interpretable GAN Controls\n- [shen2021] Closed-Form Factorization of Latent Semantics in GANs\n- [eriksson2020] Scalable Global Optimization via Local Bayesian Optimization\n- [letham2020] Re-examining linear embeddings for high-dimensional Bayesian optimization\n\n## Q2: Line 159 (logarithmic sampling) and line 161 (\"bias mitigation\") lack citations or clarification. Can you point to specific justifications and follow-ups?\n\n**Logarithmic sampling.** This is standard practice in BCI experiments to acquire more data close to where information is maximal. In our case, the changes in brain activity are more sensitive to stimulus changes near the target [watson1983,wichmann2001,leopold2005,laming2010].\n\n- [watson1983] Quest: A Bayesian adaptive psychometric method\n- [wichmann2001] The psychometric function: I. Fitting, sampling, and goodness of fit\n- [laming2010] Fechner's law: where does the log transform come from?\n- [leopold2005] The dynamics of visual adaptation to faces\n\n**\"Bias mitigation\".** While this is standard and was required in our data collection to ensure data quality, this is a current limitation of our work that will need to be addressed before expanding to online closed-loop settings. We nonetheless stress‑tested for acquisition bias by:\n\n- (i) using uniformly sampled hypotheses for our ranking task evaluation, so they are decoupled from the log‑biased acquisition and \n- (ii) performing ablation studies, removing all near‑target items within set distances of the target. Appendix Figure 6 shows that performance starts to significantly degrade compared to a non-ablated baseline once d_removed>10 (N=5432), which indicates the possibility that a non-logarithmic sampling might be an approachable proposition during an online self-calibrating experiment.\n\n## Q3: How stable is the CURSOR score across different random splits for shuffled baselines? Since the score is computed via a relative RMSE ratio, do you observe high variance depending on the shuffling?\n\nThank you very much for this question, which allowed us to deepen our analysis.\n\n**Do you observe high variance depending on the shuffling?** Standard deviation of the scores in the 10-fold cross-validation is about 2.5% of the mean at N=9234 for most methods, except MLP at about 4%.\n\n**Additional signal-to-noise evaluation.** We were curious and studied further the difference between the range of scores and the average variance, as it would indicate how robust the rankings are. For this, we computed a \"signal variation to measurement uncertainty ratio\" that is:\n\n```python\nnp.std(np.mean(scores, axis=1)) / np.mean(np.std(scores, axis=1))  # [scores are (60, 10) dimensions for the ranking task]\n```\n\nwhich is a signal-to-noise (SNR) type metric suitable for our ranking task.\nInterestingly, for S-LR and MLP, this SNR remains the same value around 0.4 for all N. LR and SVR start at the same value but linearly increase with N to significantly better values of 0.6 and 0.52, respectively, at N=9234.\n\nAnalyzed with the correlation results in Figure 3 (left) and a plot of the mean+std scores, it explains well why MLP performance is underwhelming compared to LR and SVR. MLP scores correlate with ground truth distances, but the cross-validation variance on the scores is much higher than for LR and SVR. The SNR can capture this as a lower ratio, which in turn explains the considerable variability in d-top-rank performance in Figure 3 (right).\n\nWe will add three Figures to the Appendix, along with the above analysis, to support this:\n\n- Evolution of STD as % of scores per method per N. This shows an exponential decay from 20% to 2.5% for most methods (4% for MLP).\n- Mean+std scores vs true distances aggregated for all runs---one plot for each method.\n- Signal variation to measurement uncertainty ratio (SNR) vs N for each method.\n\n## A brief discussion of these risks and possible safeguards (e.g., user consent, secure model deployment, opt-in constraints) would strengthen the paper's ethical framing.\n\nThank you for encouraging us to strengthen our ethical framing, which we do not take lightly.\n\nCURSOR links EEG to hypotheses about internal visual content; it raises the possibility of inferring mental states without explicit user intention. Physiological data can be sensitive and re‑identifiable, and consent mechanisms are often opaque in practice, which heightens the ethical salience of cognitive privacy for BCI research [solove2005taxonomy,ohm2010broken,kokolakis2017privacy,davis2024physiological].\n\nThe core risk is that models or datasets are repurposed beyond the stated task to infer mental content without informed, ongoing consent. To address this, we will expand our privacy section in the Appendix to advocate for:\n\n- Acknowledging risk explicitly: We will state that inferring internal mental content without user intent is unacceptable and a central ethical concern. [boire2000cognitive,sententia2004neuroethical]  \n- Requires opt‑in, revocable consent: Participation must be explicit, comprehensible, and withdrawable, with data erasure upon request. [berg2001informed,beauchamp2003methods,nissenbaum2004privacy]\n- Imposes purpose limitation and governance: Models and data may only be used for the approved task and context, with new consent required for secondary use and audits recorded. [european2016regulation,regulation2024ai,california2020cpra]\n- Implements data minimization and secure handling: Prefer on‑device or sandboxed processing; encrypt data in transit/at rest; set short retention windows; prohibit sharing of raw EEG. [malin2013biomedical,price2019privacy]\n- Increases transparency and accountability: Release model cards and datasheets documenting intended use, limits, failure modes, and oversight plans. [mitchell2019model,gebru2021datasheets]\n- Centers user agency: Provide a visible \"record/mute\" control, require explicit start/stop for any recording, and disable inference outside the study context by default. [davis2024physiological]\nFurthermore, our study is offline, task‑bound, and conducted with consenting volunteers; we do not perform real‑time or covert inference, and outputs are analyzed in aggregate, which we will explicitly detail as mitigations in the Appendix.\n\n[solove2005taxonomy] A Taxonomy of Privacy.  \n[ohm2010broken] Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization.  \n[kokolakis2017privacy] Privacy Attitudes and Privacy Behavior: A Review.  \n[davis2024physiological] Physiological Data and Privacy in the Wild.  \n[boire2000cognitive] Cognitive Liberty and Mental Privacy.  \n[sententia2004neuroethical] Neuroethical Concerns, BCIs, and Cognitive Autonomy.  \n[berg2001informed] Informed Consent.  \n[beauchamp2003methods] Methods and Principles in Biomedical Ethics.  \n[nissenbaum2004privacy] Privacy as Contextual Integrity.  \n[malin2013biomedical] Biomedical Data Privacy and Security.  \n[price2019privacy] Privacy in the Age of Machine Learning.  \n[mitchell2019model] Model Cards for Model Reporting.  \n[gebru2021datasheets] Datasheets for Datasets.  \n[european2016regulation] GDPR: General Data Protection Regulation.  \n[california2020cpra] California Privacy Rights Act.  \n[regulation2024ai] EU AI Act."}]

Author rebuttal:

Meta-review: This submission proposes a method for recovering a participant's mental target from their EEG and image data. The main contribution of this work is that they do not use label data or pre-trained models. The authors collected a new EEG dataset from 31 participants while they viewed synthetically generated images of faces. The proposed method is then used to predict the distances between the shown image and a set of images, as well as generate a new image that is close to the mental target.

After the author-reviewer discussion period, most of the reviewers recommended acceptance of this work. One reviewer (Reviewer VJPj) expressed concern about the proposed "error function" and the underlying motivation for its use. There were also concerns about the clarity of the submission and the use of synthetic faces; however, these were less critical. During the AC-reviewer discussion period, the other reviewers wrote that they do not see this as a major issue, rather one that needs further clarification. Therefore, provided the authors further clarify the logic motivating their choice of error function, I recommend acceptance.
Decision: Accept (poster)