# Optimizing fMRI Data Acquisition for Decoding Natural Speech with Limited Participants

Louis Jalouzot, Alexis Thual, Yair Lakretz, Christophe Pallier, Bertrand Thirion

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Louis Jalouzot, Alexis Thual, Yair Lakretz, Christophe Pallier, Bertrand Thirion. Optimizing fMRI Data Acquisition for Decoding Natural Speech with Limited Participants. NeurIPS Workshop 2025: Foundation Models for the Brain and Body., Dec 2025, San Diego (CA), United States. hal-05368522

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# Optimizing fMRI Data Acquisition for Decoding Natural Speech with Limited Participants

**Louis Jalouzot**

CEA, ENS
Université Paris-Saclay
France

jalouzot.louis@gmail.com

**Alexis Thual**

karavela.ai
France

**Yair Lakretz**

ENS, EHESS, CNRS
Université PSL
France

**Christophe Pallier**

INSERM, CEA, CNRS
Université Paris-Saclay
France

**Bertrand Thirion**

INRIA, CEA
Université Paris-Saclay
France

## Abstract

We present a systematic investigation into decoding perceived natural speech from fMRI data in a participant-limited setting. Using a publicly available dataset of eight participants [LeBel et al., 2023], we demonstrate that deep neural networks trained with a contrastive objective can effectively decode unseen natural speech by retrieving the embedding of perceived sentences from fMRI activity. We found that decoding performance directly correlates with the amount of training data available per participant. In this data regime, multi-subject training does not improve decoding accuracy compared to the single-subject approach. Additionally, training on similar or different stimuli across subjects has a negligible effect on decoding accuracy. Finally, we find that our decoders model both syntactic and semantic features, and that stories containing sentences with complex syntax or rich semantic content are more challenging to decode. While our results demonstrate the benefits of having extensive data per participant (deep phenotyping), they suggest that leveraging multi-subject data for natural speech decoding likely requires deeper phenotyping or a substantially larger cohort.

# 1 Introduction

Decoding percepts from brain activity is a central challenge in neuroscience. Foundational fMRI studies demonstrated the feasibility of decoding visual stimuli [Kamitani and Tong, 2005] and identifying simple linguistic units [Formisano et al., 2008]. Recent advances, fueled by deep-phenotyping datasets with extensive data per participant, have enabled more accurate decoding of complex stimuli like natural scenes [Ozcelik and VanRullen, 2023, Allen et al., 2022, Banville et al.] and natural language [Tang et al., 2023, Ye et al., 2025, LeBel et al., 2023]. However, a key obstacle remains: the high inter-subject variability of brain responses, which complicates developing generalizable decoders. While methods like functional alignment show promising results [Thual et al., 2023, Ho et al., 2023, Scotti et al., 2024], it is still unclear how to best leverage multi-subject data.

In this work, we focus on decoding perceived natural speech from fMRI, adopting a decoding-first approach to directly predict text representations from brain activity using a contrastive objective, a method proven effective in other brain decoding studies [Défossez et al., 2023, Scotti et al., 2023]. We make the following contributions:

1. We achieve up to 36% top-10 accuracy in sentence embedding retrieval, demonstrating that decoding performance increases with the amount of training data per participant and does not saturate even at 13.5 hours.

2. Training decoders on multiple subjects does not improve accuracy compared to single-subject models in this data regime.
3. Varying the degree of stimulus overlap between subjects has minimal impact on multi-subject decoding performance.

4. Our analysis shows that decoders capture both syntactic and semantic features, but sentences with complex syntax or rich semantic content remain more difficult to decode.

<figure><img src="image_2.png" /><figcaption>Figure 1: **Method for decoding natural speech from fMRI activity.** **A. Decoding setup** Deep Neural Networks are trained with a contrastive objective to predict text representations (derived from Large Language Models embeddings) from fMRI activity recorded as participants listened to natural speech. Key parameters include *context length c*, *lag τ*, and *smoothing κ*. **B. Subject approaches** We compare single-subject (one decoder per subject) and multi-subject (shared decoder backbone with subject-specific layers at the bottom) approaches. **C. Retrieval setup** Decoders are evaluated by ranking candidate chunks from a retrieval set based on cosine similarity between their representation and the predicted one. We compute top-10 accuracy.</figcaption></figure>
## 2 Methods

We aim to train models that predict text representations (LLM embeddings) from fMRI activity using a contrastive objective, as summarized in Figure 1. The code will be made available upon publication.

**Data and Task** We use the dataset from LeBel et al. [2023], which contains 3T fMRI recordings of 8 participants listening to stories (~6 hours/subject). Three of these subjects have an additional ~10.5 hours of data. To deal with the low temporal resolution of fMRI signal (one image is acquired every TR=2s), we chunk the perceived text into 2s segments according to acquisition windows. Our goal is to learn a mapping $f$ from fMRI images $X^s$ to LLM embeddings of the corresponding perceived chunks of text $Y^s$. To account for hemodynamic delay, we predict the text embedding $Y_t^s$ from a future fMRI volume $X_{t+\tau}^s$ with $\tau=6$s. Text representations are enriched by concatenating the transcripts of the current and $c=3$ preceding text chunks before embedding.

**Preprocessing** fMRI data was preprocessed with fmriprep [Esteban et al., 2018], temporally smoothed (averaging over $\kappa$ volumes), and standardized. This temporal averaging aims to reduce noise and improve the signal-to-noise ratio. For text, we used LLM2Vec [BehnamGhader et al., 2024] to generate $D=4096$ dimensional embeddings, which were also normalized and standardized. To reduce dimensionality and focus on informative brain regions, we selected the top 4096 voxels for each subject based on their encoding performance. Specifically, we trained a Ridge regression model on the training set to predict fMRI activity from text embeddings, and selected voxels with the highest $R^2$ score on a separate validation set, thus avoiding circularity. We show results with 4096 voxels as it yields the best performance, but similar effects are observed with whole-brain data.

**Evaluation** Performance is evaluated using top-10 retrieval accuracy on a held-out test set from cross-validation, ensuring that training and test stimuli do not overlap. For each test sample, we rank all candidate embeddings from the test set (retrieval set) by their cosine similarity to the predicted embedding. Top-10 accuracy is the frequency with which the correct text embedding appears in the top 10 candidates. For comparability, we use 5-fold cross-validation for the subjects with less data and 15-fold for those with more, so that in both cases the retrieval set has approximately 2,000 samples. The variability across folds is reported with the average and 95% confidence intervals in our figures. Moreover, we track two NLP similarity metrics between the ground truth and candidate text chunks from the retrieval set. We assess semantic similarity using GloVe bag-of-words cosine similarity on content words (nouns, verbs, adjectives). Syntactic similarity is assessed using Levenshtein distance on part-of-speech (POS) tagged chunks.

**Training** We model the mapping from fMRI to text representations using a 3-layer MLP with dropout, layer normalization, and skip connections inspired from Scotti et al. [2024]. This "Brain Decoder" is trained with a symmetric InfoNCE contrastive loss [Radford et al., 2021] (see Equation (1) in appendix for details). We use a learning rate scheduler and early stopping on the top-10 accuracy on a held-out validation set to mitigate overfitting. Hyperparameters (see Table 1 in appendix) were optimized via Bayesian search to maximize top-10 accuracy. We compare a *Single-subject* approach (one decoder per participant) with a *Multi-subject* approach (a shared backbone with a subject-specific input layer). We also investigate the effect of stimulus overlap in the multi-subject setting by varying the proportion of shared stories during training.

## 3 Results

**Baseline** We compare our results to Ye et al. [2025]'s BrainLLM, which uses a similar dataset and setup. Although their model was not trained for retrieval, we adapted their predicted embeddings to our evaluation framework. BrainLLM achieves an average top-10 accuracy of 1.6% (chance=0.5%), providing a valuable baseline for comparison.

**Single-subject decoding performance scales with data quantity** Single-subject decoding accuracy scales directly with the amount of training data (Figure 2). For the three participants with 13.5 hours of training data, we achieve an average top-10 accuracy of 27% (subject 3 reaching 36%), significantly outperforming the BrainLLM baseline (1.6%). For participants with only 4 hours of data, accuracy drops to 6%. The performance curve does not plateau, suggesting that acquiring even more data per subject could yield further gains. These results represent, to our knowledge, the first successful decoding of natural speech from fMRI using a contrastive objective. Our methodological choices were critical to this success (Figure 3). We incrementally improved a baseline MLP by adding a hemodynamic lag, using a text context window, smoothing fMRI data, switching to LLM2Vec embeddings, adopting a contrastive loss, and using a more refined "Brain Decoder" architecture.

**Multi-subject training does not improve decoding** Contrary to expectations, training on data from multiple subjects does not improve individual-subject decoding performance (Figure 4). With large models, it can even be detrimental,
<figure><img src="image_3.png" /><figcaption>Figure 2: **Impact of training data amount on single-subject performance.** Cross-validated top-10 accuracy of single-subject decoders. The retrieval set contains ~2k samples. Chance level (0.5%) and the BrainLLM baseline (1.6%) are shown for comparison.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Figure 3: **Setup comparison.** Impact of various elements of the decoding setup on decoding performance. We start from a very crude version of our setup, namely "Base", which is essentially a simple MLP trained with MSE loss on BERT latents. Then each row corresponds to the previous setup with a modification described by its blue label.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Figure 4: **Impact of the number of training subjects.** Multi-subject decoders were trained for all 255 combinations of the 8 subjects. For each subject (color), we plot the results for the combinations which achieved best accuracy for each number of subjects. We show results for small (left) and large (right) decoders.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Figure 5: **Impact of training stimuli overlap.** We train multi-subject decoders while varying the ratio of overlapping stimuli between subjects. The graphics display the increment in accuracy over single-subject decoders for small (left) and large (right) decoders.</figcaption></figure>

yielding lower accuracy than single-subject decoders. The effect is less pronounced for smaller models, but gains are marginal. This suggests that in our data regime (few subjects, deep phenotyping), the model struggles to overcome inter-subject variability, even if it generalizes to new stimuli for a given subject. Furthermore, we found that the degree of stimulus overlap between subjects during training has a negligible effect on multi-subject decoding performance (Figure 5), indicating that more target space diversity did not help the decoder generalize.

**Decoders model both syntax and semantics** We computed syntactic and semantic similarity between ground-truth text and retrieved candidates as a function of their embedding similarity rank to the decoder prediction (Figure 6).
<figure><img src="image_7.png" /><figcaption>**Figure 6: Profiles of average syntactic/semantic similarities.** Syntactic (left) and semantic (right) similarities between ground-truth and candidate chunks, sorted by rank. Similarities are normalized by a random baseline.</figcaption></figure>

Reassuringly, both similarities decrease as the rank of the candidate increases. This suggests that the decoders effectively model syntax and semantics and are not only relying on superficial features to achieve such performance. Also, the profile for syntactic similarity drops off more sharply than for semantic similarity, indicating the decoder is slightly better at differentiating syntactically dissimilar chunks than semantically dissimilar ones.

**Simple text is easier to decode** We analyzed the linguistic properties of well-decoded versus poorly-decoded texts. A qualitative analysis of the 10 best and worst decoded stories (see Table 2 in appendix) conducted with Gemini [Reid et al., 2024] revealed that stories with simple, conversational language, short sentences, and a direct narrative voice were decoded more accurately than those with complex syntax, formal language, and abstract ideas.

# 4 Discussion and Limitations

Our results achieve the highest top-10 retrieval accuracy reported for speech decoding and also provide clear insights for optimizing fMRI data acquisition with a small number of participants for this task. We demonstrate that a decoding-first approach with contrastive objectives can effectively predict LLM-derived text representations from fMRI data. Performance scales directly with the quantity of per-participant data without saturating, even with 13.5 hours of data.

Interestingly, multi-subject training failed to improve performance within our data set of eight individuals (with extensive data from only three), suggesting that inter-subject variability is too high for current decoders. This finding aligns with recent visual decoding findings, which indicate that increases in per-subject data outperform increases in subject count [Banville et al.]. This suggests that performance gains may require substantially larger cohorts or deeper phenotyping. Our finding that stimulus overlap between subjects has a minimal impact further supports the idea that inter-subject variability, rather than stimulus diversity, limits multi-subject performance.

Our analysis revealed that decoders leverage syntactic structure and semantic content, and they struggle more with linguistically complex stories. Stories written in simple, conversational language with short sentences were easier to decode than those with complex syntax and abstract ideas. This suggests the need for an investigation into training data biases and mitigation strategies.

Overall, our findings strongly suggest that collecting extensive data from a smaller number of participants is more effective than collecting less data from a larger number of subjects. This approach is especially beneficial when working with small participant groups, as is often the case in neuroimaging studies.

One limitation is that embeddings are predicted rather than text being reconstructed, though this avoids evaluation metrics dependent on generative models. fMRI's low temporal resolution remains a challenge. The main limitation is the quantity of the training data. To advance the boundaries of brain decoding, we advocate for larger datasets.

# 5 Acknowledgements

This work was performed using HPC resources from GENCI-IDRIS (Grant 2024-AD011016055) and benefited from state aid managed by the Agence Nationale de la Recherche ANR-24-CE17-1427 and innovation under the Specific Grant Agreement HORIZON-INFRA-2022-SERV-B-01.
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Repina, M. Wu, L. Knight, M. Georgiev, H. Lee, H. Askham, A. Chakladar, A. Louis, C. Crous, H. Cate, D. Petrova, M. Quinn, D. Owusu-Afriyie, A. Singhal, N. Wei, S. Kim, D. Vincent, M. Nasr, I. Shumailov, C. A. Choquette-Choo, R. Tojo, S. Lu, D. d. L. Casas, Y. Cheng, T. Bolukbasi, K.-i. Lee, S. Fatehi, R. Ananthanarayanan, M. Patel, C. Kaed, J. Li, J. Sygnowski, S. Belle, Z. Chen, J. Konzelmann, S. Pöder, R. Garg, V. Koverkathu, A. Brown, C. Dyer, R. Liu, A. Nova, J. Xu, J. Bai, S. Petrov, D. Hassabis, K. Kavukcuoglu, J. Dean, O. Vinyals, and A. Chronopoulou. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. ArXiv, Mar. 2024.

P. S. Scotti, A. Banerjee, J. Goode, S. Shabalin, A. Nguyen, E. Cohen, A. J. Dempster, N. Verlinde, E. Yundler, D. Weisberg, K. A. Norman, and T. M. Abraham. Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors, May 2023. URL http://arxiv.org/abs/2305.18274.

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Contrastive Loss

We use the symmetric InfoNCE loss, a contrastive loss function popularized by CLIP [Radford et al., 2021]. Given a batch of N pairs of predicted text embeddings from fMRI $\hat{y}_i$ and ground-truth text embeddings $y_i$, the loss is defined as:

$$ \mathcal{L} = -\frac{1}{2N} \sum_{i=1}^{N} \left( \log \frac{\exp(s(\hat{y}_i, y_i)/\tau)}{\sum_{j=1}^{N} \exp(s(\hat{y}_i, y_j)/\tau)} + \log \frac{\exp(s(\hat{y}_i, y_i)/\tau)}{\sum_{j=1}^{N} \exp(s(\hat{y}_j, y_i)/\tau)} \right) \quad (1) $$

where $s(\cdot, \cdot)$ is the cosine similarity and $\tau$ is a learnable temperature parameter. The first term inside the sum is the loss for predicting the correct text embedding given an fMRI-derived embedding, among all text embeddings in the batch. The second term is the loss for predicting the correct fMRI-derived embedding for a given text embedding.
Supplementary Tables

<table><caption>Table 1: **Hyperparameters of the DNNs** Comprehensive list of the hyperparameters used in the study. They were determined through a Bayesian grid search maximizing cross-validated top-10 accuracy on subjects 1, 2, and 3 in the multi-subject setup. In this setup small decoders (hidden dimension 64) have 250k parameters in their backbone and 250k (per subject) in the subject-specific layers. Large decoders (hidden dimension 4096) have 7e7 in addition to 1.7e7 (per subject) parameters.</caption><thead><tr><th>Hyperparameter</th><th>Value</th><th>Description</th></tr></thead><tbody><tr><td colspan="3"><strong>Data Configuration</strong></td></tr><tr><td>Context Length</td><td>3</td><td>Number of preceding text chunks concatenated (= 6s)</td></tr><tr><td>Temporal Smoothing</td><td>4</td><td>Number of preceding brain volumes averaged (= 8s)</td></tr><tr><td>Lag (s)</td><td>6</td><td>Delay between a brain volume and the target chunk</td></tr><tr><td>Top Encoding Voxels</td><td>4096</td><td>Number of voxels selected based on encoding performance</td></tr><tr><td colspan="3"><strong>Brain Decoder Configuration</strong></td></tr><tr><td>Hidden Dimension</td><td>64 or 4096</td><td>Size of the hidden layers (both are considered in the experiments)</td></tr><tr><td>Number of Linear Layers</td><td>1</td><td></td></tr><tr><td>Number of Residual Layers</td><td>1</td><td></td></tr><tr><td>Total number of layers</td><td>3</td><td>Subject specific layer + Linear + Residual</td></tr><tr><td>Normalization Type</td><td>Layer</td><td></td></tr><tr><td>Activation Function</td><td>GELU</td><td></td></tr><tr><td colspan="3"><strong>Training Configuration</strong></td></tr><tr><td>Temperature</td><td>0.7</td><td>Temperature of the contrastive loss</td></tr><tr><td>Learning Rate</td><td>1e-4</td><td></td></tr><tr><td>Weight Decay</td><td>5e-4</td><td></td></tr><tr><td>Patience</td><td>20</td><td>Number of epochs with no improvement before early stopping</td></tr><tr><td>Scheduler Patience</td><td>5</td><td>Number of epochs with no improvement before reducing the learning rate</td></tr><tr><td>Scheduler Factor</td><td>0.5</td><td>Factor by which the learning rate is reduced</td></tr><tr><td>Batch Size</td><td>1</td><td>Number of stories per batch</td></tr><tr><td>Max Epochs</td><td>200</td><td></td></tr></tbody></table>
<table><caption>Table 2: Qualitative analysis of the impact of semantics and syntax We use Gemini [Reid et al., 2024] to analyze the commonalities and differences between the 10 best and 10 worst performing stories in terms of semantics and syntax. Stories are sorted by the accuracy obtained from subject 3 and we display the mean/min/max accuracy for each group of stories.</caption><thead><tr><th>Group</th><th>10 best decoded stories</th><th>10 worst decoded stories</th></tr></thead><tbody><tr><td>Feature</td><td>Personal experience</td><td>Ideas &amp; Reflection</td></tr><tr><td>Performance</td><td>Top-10 accuracy: mean 51%, min 44%, max 59%</td><td>Top-10 accuracy: mean 15%, min 5%, max 21%</td></tr><tr><td>Narrative Voice</td><td>Conversational, informal, direct address ("you know," "I mean")</td><td>More formal, polished, less direct address</td></tr><tr><td>Sentence Length</td><td>Primarily short, choppy sentences for immediacy and emotional impact</td><td>Longer, more complex sentences reflecting intellectual exploration</td></tr><tr><td>Vocabulary</td><td>Colloquial language, contractions, slang</td><td>Wider range of vocabulary, less colloquialism, more sophisticated phrasing</td></tr><tr><td>Use of Dialogue</td><td>Frequent, natural-sounding dialogue to advance the narrative and express emotion</td><td>Dialogue used, but less prevalent and often serves an illustrative function</td></tr><tr><td>Sentence Structure</td><td>Loose sentence structure with emphasis on emotion and immediacy</td><td>More complex sentence structures, often with clauses and sub-clauses</td></tr><tr><td>Tone</td><td>Immediate, intimate, emotionally charged, often reflecting raw feelings and vulnerability</td><td>Reflective, analytical, measured, with a more intellectual distance and a focus on conveying insights</td></tr><tr><td>Descriptive Language</td><td>Emphasis on sensory details and vivid imagery.</td><td>More use of metaphor and simile to add a higher level of detail and comparison to the text</td></tr><tr><td>Rhetorical Questions</td><td>Frequent use of rhetorical questions</td><td>Less use of rhetorical questions</td></tr><tr><td>Emphasis</td><td>Primarily to drive the narrative with a strong focus on the characters' internal experiences</td><td>Primarily to reflect, analyze and offer insights</td></tr><tr><td>Stories</td><td>kiksuya, itsabox, thefreedomridersandme, comingofageondeathrow, hangtime, life-imagined, cautioneating, thatthingonmyarm, fromboyhoodtofatherhood, threemonths</td><td>notontheusualtour, breakingupintheageof.google, jugglingandjesus, theadvancedbeginner, alternateithicatom, forgettingfear, avatar, theshower, treasureisland, bluehope</td></tr></tbody></table>
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**Question:** Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.

**Answer:** [Yes]

**Justification:** The paper's core methodology relies on predicting LLM-derived text representations, and it explicitly states the use of LLM2Vec for this purpose in the Methods section. It also declares the use of Gemini for the qualitative analysis in the appendix (Table 2).

**Guidelines:**

* The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.

* Please refer to our LLM policy (https://neurips.cc/Conferences/2025/LLM) for what should or should not be described.