BrainUICL: An Unsupervised Individual Continual Learning Framework for EEG Applications
Authors: Yangxuan Zhou
Sha Zhao
Jiquan Wang
Haiteng Jiang
Shijian Li
Tao Li
Gang Pan
Venue: ICLR 2025 Poster (2025)
Keywords: Continual Learning; EEG Applications
Primary area: applications to neuroscience & cognitive science

Abstract: Electroencephalography (EEG) is a non-invasive brain-computer interface technology used for recording brain electrical activity. It plays an important role in human life and has been widely uesd in real life, including sleep staging, emotion recognition, and motor imagery. However, existing EEG-related models cannot be well applied in practice, especially in clinical settings, where new patients with individual discrepancies appear every day. Such EEG-based model trained on fixed datasets cannot generalize well to the continual flow of numerous unseen subjects in real-world scenarios. This limitation can be addressed through continual learning (CL), wherein the CL model can continuously learn and advance over time. Inspired by CL, we introduce a novel Unsupervised Individual Continual Learning paradigm for handling this issue in practice. We propose the BrainUICL framework, which enables the EEG-based model to continuously adapt to the incoming new subjects. Simultaneously, BrainUICL helps the model absorb new knowledge during each adaptation, thereby advancing its generalization ability for all unseen subjects. The effectiveness of the proposed BrainUICL has been evaluated on three different mainstream EEG tasks. The BrainUICL can effectively balance both the plasticity and stability during CL, achieving better plasticity on new individuals and better stability across all the unseen individuals, which holds significance in a practical  setting.

Published as a conference paper at ICLR 2025
BRAINUICL: AN UNSUPERVISED INDIVIDUAL CON- TINUAL LEARNING FRAMEWORK FOR EEG APPLICA- TIONS
Yangxuan Zhou' Sha Zhao Jiquan Wang 12, Haiteng Jiang 34.1, Shijian Li
Tao Li34,1 .aang Pan
State Key Laboratory of Brain-machine Intelligence, Zhejiang University
"oolleee of Computer Science and Technology, Zhejiang University
3 *Department of Neurobiology, Affiliated Mental Health Center Hangzhou Seventh People's 's Hospital, Zhejiang University School of Medicine
"MOE Frontier Science Center for Brain Science and Brain-maching Integration, Zhejiang University
[zyangxuan, szhao, wangjiquan, h. jiagg ezju.edu.co edu. cn; (shijianli, litaozjuse, gpan ) jz edu. cn;
ABSTRACT
Electroencephhalggrahyy (EEG) is a non-invasivee brain-computer interface tech- nology used for recording brain electrical activity. plays an important role in human life and has been widely uesd in real life. including sleep staging, emotion recognition, and motor imagery. However, existing EEG-related models cannot be well applied in practice, especially in clinical settings, where new patients with individual discrepancies appear every day. Such EEG-based model trained fixed datasets cannot generalize well to the continual flow of numerous un- seen subjects in real-world scenarios. This limitation can be addressed through continual lcarning (CL), wherein the CL model can continuously learn and ad- vance over time. Inspired by CL. we introduce a novel Unsupervised Individual Continual Learning paradigm for handling this issue in practice. We propose the BrainUICL framework, which enables the EEG-based model to continuously adapt the incoming new subjects. Simultaneously, BrainUICL helps the model ab- sorb new knowledge during each adaptation, thereby advancing its generalization ability for all unseen subjects. The effectiveness of the proposed BrainUICL has been evaluated on three different mainstream EEG tasks. The BrainUICI. can effectively balance both the plasticity and stability during CI., achieving better plasticity on new individuals and better stability across all the unseen individuals, which holds significance in a practical setting. The source code available at https: / httggtttttuuug.g/////// com wwtpxxxawbbbbbeeoee www.baabeeeeraaniiito BrainnUICL
INTRODUCTION
Fleetroencephallggappyy (EEG) a non-invasive brain-computer interface (BCT) technology, record- ing brain electrical activity through electrodes placed on the scalp. Due to the non-invasive nature and relatively high temporal resolution, EEG plays an important role human life and has been widely used in practice, especially in clinical settings (i.c., sleep staging (Perslev et al. 2019 Aboalayon et al. 2016), emotion recognition (Song et al.
2018 Cowie et al. 2001). motor imagery (Taaar & Halici 2016) and disease diagnosis (Petit al. 2004 Jeong, 2004). However, existing EBG-related models cannot perform well in real life. In practical situations, there are gradually varying new subjects every day. Moreover, there significant individual discrepancies (i.e., physiological structures, physical characteristics) among different subjects. Such EEG-based models trained on fixed datasets cannot generalize well to the new unseen individuals. The above limitation motivates us address this issue for practical applications. Fortunately, this problem can be reduced to continual learning (CL),
"Corresponding authors.

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Source Domain
Better Plasticitty Doe Target Individuals
Better Stabiliity for Unseon Individusis
Preczined Model
Before CL After CL
P- Adaptabiliity
Other Methods
$
Time
Contimual Individual
Continual Domain Adaptation
Unseen Individhuls
8- Generaliration Ability
Unseen Individuals
Gesemiizabll Model
Indinciduaal Flow
Continual Individual Flow
(P)
(b)
[c)
Figure I: (a). The proposed paradigm of Unsupervised Individual Continual Learning: (b). Continual domain adaptation for better Plasticity; (c). Generalizable model for better Stability.
wherein the CL model can learn and advance by continuously absorbing new knowledge. The major challenge in CL is to overcome the Stability-Plastion
(SP) dilemma (Mermillod et al., (203)) with Plasticity (P) denotes the model's adapting ability to newly emerging individuals, while Stability (S) indicates the model's generalization ability to both previously seen and unseen individuals (i.c., new subjects). Inspired by CL, and considering that the incoming individuals lack ground truth labels, we propose novel Unsupervised Individual Continual Learning (UICL) paradigm for handling EEG tasks in practical applications shown in Fig. (a). Notably, considering the individual discrepancies. we treat each subject as distinct individual domain (Yang et al.) (023)) in our study. As shown in Fig. (a), the pre-trained model is required to continuously adapt to multiple individual target domains one by one while absorbing the new knowledge to advance, and finally becomes universal expert for all unseen individuals. Our main objectives are twofold: (1) Better Plasticity: the model can adapt well to each new subject from the continual individual flow shown in Fig. (b). (2) Better Stability: the model can achieve stronger generalization ability on all the unseen subjects after continuously learning the knowledge from the continual individual flow shown in Fig. (c).
However, is not a straightforward task to enable the model to continuously adapt well to multiple newly emerging subjects (better P) and simultaneously improve its generalization ability for all unseen subjects (better S). There are three main reasons. First, a better Plasticity is difficult to obtain, because the individual discrepancies among the continual individual flow lead to continual domain shifts between the distribution of the source domain and that of the individual target domains. Second, the Stability could decrease on all unseen individuals, because the model may be overfitted some individual target domains for a better plasticity. What's 's worse is that if the model adapts to some outlier individuals, the model may dramatically degrade in performance and may not be able to recover during subsequent continual adaptation (Wang et al. 2022). Third, balancing the plasticity and stability challenging, which means the model needs to ensure its adaptability to new individuals while improving the generalization ability on all the unseen individuals. There have been some existing studies addressing similar issucs, but they are not so adaptable in practice. For example, the studies Wang et al. (2022); y [Taufique et al. (2021: 2022 face small quantity of varying target domains, and tackle the SP dilemma in scenarios such as object detection and image classification. They typically assume that the domain change in continual batches minimal and conduct study at the sample level. However, the practical scenario is quite different, where there is a continual flow of numerous new subjects and there exist significant individual domain changes. Meanwhile, in real life, it is required to be conducted at the individual level (i.e., testing the EEG data of only one person at a time) instead of the sample level.
To achieve both better plasticity and stability, we propose novel EEG- Unsupervised Individual Continual Learning framework, called BIa.. It is well-suited to real-world scenarios where a large number of unseen and unordered individuals continuously emerge, enabling the model to continuously adapt to a long-term individual flow in a plug-and-play manner, while also balancing the SP dilemma during such CL process. We have designed two novel modules: the Dynamic Confident Buffer (DCB) and Cross Epoch Alignment (CEA) to tackle the aforementioned challenges. Specifically, the DCB employs selective replay strategy that ensures the accuracy of labels for replay samples in an unsupervised setting while maintaining the diversity of these samples. The CEA module innovatively aligns the incremental model across different time states to prevent overfitting,
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ensuring that the incremental model remains unaffected by varying learning trajectories, which is particularly relevant given that continual flows are unordered in real-world scenarios. Besides, is worth pointing that BrainUICL is easy to be implemented without any modifications to the model structure. The contributions of this paper can be summarized as follows:
We first explore the concept of the Unsupervised Individual Continual (earning(UICI in EEG-related applications, which is well-suited to the real-world scenario and meets the practical needs in real life. The proposed BrainUICL. framework can effectively balance Stability-Plasticity dilemma during the CL process.
We design novel DCB and CEA modules to dynamically adjust the adaptation process during the long-term individual continual learning, overcoming the challenges of overlitting and preserving the knowledge learned from the past individual flow.
Validated on three different mainstream EEG tasks. Brain enables the model to adapt well to continual individual flow (better Plasticity), and achieve stronger generalization ability on all unseen indiiidualsbbetter Stability), resulting in win- win gain.
2 METHODOLOGY
2.1 PROBLEM SETUP AND PRELIMINARIES
Facing practical applications, we try to make the model not only adapt well continuously incoming new subjects, but also generalize well to all the unseen subjects, taking advantage of the idea of unsu- pervised individual continual learning. We consider each subject as an individual domain. Formally,
given multiple labeled individual domains (i.e., source domain, training set) D==(X's V's " with Nss subjects, multiple unlabeled individual target domains (i.e., continual individual flow, incremental set) Dy=[2(+ WT i= with Ny subjects, and multiple labeled test domains (i.e., generalization set) )i with subjects, where NG < Vs < Ny. Different individual target domains follow non-identical data distributions P(Di)) * P(D+), where ii NG. We denote the incremental model as M and its probability distribution as P(M), where MMo denotes the initial model trained from the source domain Ds. and M, denotes the current updated model when has adapted to D In our UICL setting, we consider the incremental model M is available with only an individual target domain at once. When M, + _Ma. after each round updating, the corresponding distribution change can be described as Ap A(P(M;;. P(M++1)). During the CL process, the BrainUICL will gradually increase the penalty on the incremental individual target domain with continual update iterations, leading to smaller distribution change, i.e., lim,-++oo Ap 0. The objective of BrainUICL is to enable the incremental model M, trained from a small source domain Ds, to adapt multiple individual target domains Df and improve the generalization ability for the unseen test domain D& after continuously absorbing new knowledge. During each round iteration, our goal can be described follows:
Do=(X. )a)m
min 8
E (x)D)))D ((M(X)) ( + E ((t...ee)-De-(((a)....
where M, parameterized by M, Here, penalty terms for model updates. In other words, the penalty imposed by BrainUICL. on continual individual flow could effectively prevent the model from overfitting incremental individual target domain Di,, while learning new knowledge to improve the model's generalization ability on Dc.
Ve) can be understood as the
2.2 OVERVIEW
Inspired by the babearsal-based CL methods (Rebuff et al., 2017 Lopez-Paz & Ranzato 2017 Castro et al.) 2018) Aljundi et al., 2019), which alleviate catastrophic forgetting by replaying subset of past samples from a storage center, we also adopt a replay-based strategy in this work. As shown in Fig. 2 the workflow of the BrainUICL framework can be divided into three parts when an incremental individual comes in. First, producing pseudo-labels: since the incoming subject is without labels, self-supervised learning (SSL) is needed to provide pseudo-labels. We only preserve the confident pseudo-labels whose prediction probabilities are higher than the confidence threshold, for subsequent fine-tuning. Second, updating incremental models M: for each batch
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Mi-1
Copy
M
SSL Training
Guiding Model
Produce Psoudo label
M,
Incremental Individunl
Teyus
Finish Training
Next
1MM
Combine
y/
M
Classity
Ús
Continual Individual Flow
Update
<- Every Egochs >LKE
C
Update Stonge
An MM
Data Replay
M
Real-Porrado Label Replay
Storage
Storage
T
Ti41
Figure 2: The workflow of the proposed BrainUICI. framework. The latest incremental model M;- arrives at the T7 time step. After adapting to the current individual from the continual flow, the model updates from M to M,, and arriving to the next time step T++1 to adapt to the next individual.
of the incremental individual data X7. the storage center provides a real-pseudo mixed buffer X's with the same size for joint training. The details of this procedure are in Sec. 2.3.2 We input Xi and Ag to the model M and then obtain the hidden features Fi and Fg. Throughing the same classifier, the corresponding prediction if and ýæ can be obtained, respectively. For the prediction 97 of the incremental individual, we employ the confident pseudo- 77 generated in the first step to compute the loss Le,, Similarly, for buffer's prediction ÚB, we adopt the corresponding replayed label ys to compute the loss Len Every epochs of fine-tuning. we align the hidden features Fs and F/g. which are generated from models M and M at different temporal states, by using the Kullback-Leitn Leibler divergence to compute the loss LKc- Notably, Mi_1 and M denote the incremental model at the e-th and the (e+2)-th fine-tuning epoch, respectively. More details of this procedure are in Sec. 2.3.3 Third, updating the storage center: after adapting to the incremental individual target domain D), the model has been updated from M,- to M,, Then utilize the current model M; to predict the previous individual's sample Xi. and preserve the pseudo-laheled samples with high quality into the storage center.
2.3 BRAINUICL
In this study, we employ identical model architectures across each downstream EEG task, thereby ensuring equitable validation of the effectiveness our proposed BrainUICL framework. The model is equipped with feature extractor to extract EEG features and temporal encoder to learn the temporal information from the EEG sequences. Given a labeled source domain Ds (i.e., multiple labeled individual domain, training set), we pretrain the model by minimizing the cross-entropy loss. The detailed model architecture diagram and pretraining process are listed in the Appendix. [ After pretraining the model on the source domain Ds. have obtained the initial incremental model Moo Currently, given a continual individual flow (i.e., incremental set), which contains Ny unlabeled individual target domains, the model Mo is required to adapt each individual target domain X one by one (i.c., .M + ... + M; + + M, After each adaptation, the model is validated on the test domains (i.e., generalization set) evaluate its generalization ability.
2.3. SSL TRAINING FOR SUBJECT-SPECING PSEUDO LABEL
Commonly, the existing unsupervised domain incremental learning (Domain-IL) studies (Taufique) (et al., 2022; Xie et al. 2022 Lamers et al., 2023) employ cluster-based techniques to provide the pseudo-labels in other areas. However, cluster-based are not effective for EEG signals due to their low hignal-to-nois ratio (Goldenholz et al. 2009). Considering the sequential nature of EEG signals, opt for the Contrastive Predictive Coding (CPC) (Oord al.) (018)) algorithm to conduct self- supervised training. Specifically, whenever an incremental individual arrives, we fine-tune the guiding model, which is copied from the latest incremental model M using its samples with the CPC
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algorithm. By doing so, we believe the guiding model can initially fit the incremental individual, thereby producing pseudo labels with higher-qualie quality. Furthermore, we have set a confidence threshold & to filter out low-quality pseudo-labels. More details about the CPC is listed in the Appendix. [
2.3.2 DYNAMIC CONFIDENT BUFFER
The selection mechanisms of the buffer samples are crucial for those rehearsal- CL meth- ods. The common option is to store all encountered samples beforehand and randomly select a subset for replay (Castro et al.) 2018). Besides, the selection based on FIFO (first-in, first-out) (Taufique et al.] 2022). minimum logit distance (Chaudhry et al. 2018a), minimum confidence (Hayes & Kanan 2021), , etc., also commonly employed for replay. However, these buffer sam- ple selections, which primarily rely on past incremental samples, are not suitable for our UICL setting. Even though employ the confidence threshold & increase the quality of pseudo-labels, still inevitably introduces noise, result- ing in error accumulation during the fine- tuning stage without the help of true la- beled samples. To tackle this, we pro- pose a selected storage and real-pseudo mixed replay strategy. Specifically, our storage center consists of two parts: the storage of true labeled samples from the training set Strue {Ks. and the storage of pseudo-labeled samples from the continual individual flow Spscudo
Algorithm 1: UICL Algorithm
Ne Input: {x3, Vs' Me ( y (2) Vala Output: .M
Incremental Model Pretraining: Pretrain the model Ma using the source data X's
Unsupervised Individual Continual Learning:
for i -- 1 to NG do
Generate the guiding model Mg. copied from the latest incremental model M, Optimize Mg by minimizing Eq.
{&y,, (. At each time step. for the new coming batch of incremental individ- uals, we select buffer samples from both Strue and Spsendo in an 8:2 ratio, respec- tively. can be described as follows:
Generate confident pseudo labels By by Mgi
i= then
XB + XSESimee
else
X5 = XSEStru U ESpenodod
(2)
Xs = XSES U.
Here, we utilize relatively more real la- beled samples from the Strue, and rela- tively less previously preserved pscudo- labeled samples from the S Speeddo for re- play. It can be regarded as another form of penalty terms incorporated on the incre- mental individuals, as we solely employ a small number of past incremental samples to maintain the diversity of buffer sam- ples. After each round of updating (i.e., M;- + M.), we utilize the current in- cremental model M, to predict the i-th individual and update its pseudo-labeled samples, whose prediction probability is
end
for
j - 1 t0 10 do
Input Xs. Mt to MM and obtain Uıs 04: Optimize M;-1 by minimizing Eq. (4) 2 0 then
Optimize M;-1 by minimizing Eq. 3): end end
Obtain current incremental model M,; Input Xi to M, and generate confident pscudo-labeled samples (d²z 521)); Update storage Spscudo Specudo U (27.3))
end
higher than the confidence threshold &2. into the storage Spscudo (i.c., Spseudo {(Eg. 5 D (.), J.. 5ー }f)) ))..
Due to the limited number of samples from the source domain and the preservation of only partial samples from incremental individuals, it is acceptable incur additional storage costs during the continual learning process.
2.3.3 CROSS EPOCH ALIGNMENT
During each round of the individual domain adaptation, the incremental model may excessively overfit to some specific individuals without any constraints, which leads to the catastrophic forgetting of previously learned information. This problem can be especially exacerbated if the model encounters outlier individuals whose EEG signals significantly abnormal. |Wang al. (2022) employed stochastic restoration to randomly restore some tensor elements back to their initial weights. However, this approach may result in certain crucial parameters being completely reset. In our study,
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propose the cross epoch alignment method to overcome the overfitting while taking the preservation of model parameters into consideration. Specifically, given the same incremental model with different temporal states M 1 and M here denotes the current e-th training epoch. We denote their probability distribution as 1) and P(M respectively. Every two epochs, we employ Kullback-Leibler (KL) Divergence loss to align the gap between P(M and P(M,-?) follows:
8; Xg) = (PM()(() P(M{1))
(3)
where denotes the optimization parameters of the model. By aligning the distribution of the previous model state, the network prevents itself from deviating too much even when encountering outlier individuals, enabling the model achieve better stability. Moreover, avoiding overfitting provides more capacity for further continual domain adaptation, leading to better plasticity.
2.3.4 OVERALL LOss FUNCTION
We use the enoss-entropy loss for both buffer samples and incremental individual samples as follows:
Lc(Mi-1,, KB, Nj. ub) = Lce + +acer
Σ yB. log U8. + a(- Σ Tr. log 07.)) (4)
0.01, a a= 0.1 0.
{
i< n i2
(5)
0.100
Here, a a crrper-paramete that gradually de- creases the continual learning process pro- gresses shown in Fig 3 And denotes the i-th individual and represents the number of indi- viduals involved in the training set (i.e., Nss. In other words, the penalty incremental individ- uals gradually increases during the CL. Stated differently, this setting is for the model to pro- gressively stabilize itself after it has acquired enough knowledge from the continual individual flow. The overall loss as follows:
0.001
Figure 3: The hyper-parameter a controls the in- fluence of incremental individuals the model.
Lc, Lc + Exc.
E 2
Loverall =
(6)
e 2
where denotes the e-th fine-tuning epoch. The overall algorithm is illustrated in Algorithm,
EXPERIMENT
3.1 EXPERIMENTAL SETUP
As shown in Tab. 1. we employ three mainstream EEG tasks for evaluation: sleep staging, emotion recognition and motor imagery. Specifically, for each EEG task, conduct study using publicly available dataset, namely ISRUC (Khalighi et al. 2016), FACED (Chen et al. 2023). and Physionet-MI (Schalk al., 2004), respectively.
ISRUC, a five-class sleep staging database consists of three sub-groups. We specifically selected sub-group 1, which involves all-night EEG signals from 100 individuals. We excluded subjects and 40 due to some missing channels. The sleep recordings are band-pass pass filtered (0.3Hz-35Ha) and resampled to 100Hz. FACED, an emotion recognition database comprises 32-channel EEG recordings from 123 subjects when they watched 28 emotion-elicitation video clips, and it involves nine emotion categories. The recordings are resampled to 250Hz. Physionet, a motor imagery database comprises 64-channel EEG recordings from 109 subjects, covering four motor imagery tasks. We excluded subjects (38, 88, 89, 92, 100, 104) due differences in the sampling rate or duration of the performed tasks. The recordings resampled 160Hz. More details of the datasets are listed in the Appendix. [E]
For incremental model pretraining, set the number of training epoch 100 and the learning rate set to le-4, For the SSL training and the subsequent fine-tuning we both set the epoch to 10. The default learning rate for these process are set le-6 and le-7, respectively.

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Table 1: Overview of the processed EEG datasets.
BCI Task
Dataset
Subject
Sampling
Class
Channel
Pretraining
Generalization
Ineremental
Sleep Staging
ISRUC
Emotion Recognition
FACED
123
250
9
32
61
Motor Imagery
Physionet
103
160
4
64
32
20
51
Table 2: Overview performance of BrainUICL three downstream EEG tasks. The results of the Plasticity are evaluated on the incremental set (i.e., continual individual flow) and the results of the Stability are evaluated the generalization set.
Evaluation Plasticity
Evaluation of Stability
Average ACC
Average MF1
AAA
AAFI
Mo
M;-1
MM
Mo
Mi-1
M
Mxr
Mo
MAr
ISRUC FACED
65.1
72.8
75.1 (+10.0)
57.6
67.1
70.0 (+13.4)
72.0
74.1 (+2.1)
69.9
72.1 (+2.2)
24.2
38.9
40.3 (+16.1)
17.6
35.2
37.1 (+19.5)
24.0
36.5 (+12.5)
18.7
34.5 (+15.8)
Physionet
46.1
47.4
48.2 (+2.1)
44.6
46.3
(42.8)
46.9
48.8 (+1.9)
46.3
48.5 (+2.2)
Based on our UICL setting, each dataset is divided into three parts: pretraining, incremental and generalization sets, with a ratio of 3:5:2. The pretraining set is used to pretrain the initial incremental model MM. The incremental set (i.e., continual individual flow) is used for individual continual domain adaptation and for evaluating the model's plasticity. During this step, the incremental model needs to continuously adapt to each unseen individual one by one. The generalization set is used to evaluate the model's 's stability after each round of incremental individual adaptation is completed. The detailed UICL processes are listed in the Appendix. Fig.
We adopt four metrics to evaluate the stability and the plasticity of our proposed method. For each new incremental individual, we employ Accuracy (ACC) and Macro-F1 (MF1) to evaluate its performance. Subsequently, we compute the Average ACC and Average MF1 across all incremental individuals involved in the continual individual flow as metrics to evaluate the plasticity of our model. After each round of individual domain adaptation, we evaluate the stability of the updated model on the generalization set using Average Anytime Accuracy (AAA) (Caccia et al. 202T) and Average Anytime Macro-F1 (AAF1) metrics. Here, AAA; and AAFI, denote the average ACC and the average MF1 of incremental models MMM M:} on the unseen individuals (i.c., generalization set), respectively. The detailed formulas are follows:
Nig ACCO
NG MF10% (7)
Σ AAA, = 1 j=1
AAFI
NG
NG
1=1
where i denotes the i-th incremental individual (i.e., the current individual) and No denotes the number of individual involved in the test domain 'G (i.e., the generalization set). V% and 5% denote the true labels and the corresponding predictions of the model, respectively. Notably, for the subsequent comparison and ablation studies, we conduct multiple runs by randomly shuffling the input order of the continual flow (maintaining the consistency of the data partitions) to conduct a statistical evaluation. Therefore, calculate the mean and variance of the results(i.e., ACC, MF1, AAA, AAF1) from each run to provide statistical results.
3.2
RESULT ANALYSIS
3.2.1
OVERVIEW PERFORMANCE
We have conducted our BrainUICL framework on three different downstream EEG tasks shown in Tab. 2 Specificallyy for i-th incremental individual, we compute its personal performance through the same model at three different temporal states (i.e., Mo. MM, M.). After each adaptation, we measure the latest model's 's stability on generalization set. Here, My denotes the initial model. M,-1 and M; represent the incremental model before and after adapting to the i-th individual, respectively. Myy denotes the final model after continual adaptation to all incremental individuals. The results demonstrate that our method can achieve both the better plasticity and stability. For
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plasticity, after each round iteration, the latest model M, improved the performance the incremental individual compared to the previous state of the model M,-1- When compared to the initial model Mo, there is significant improvement in the performance of incremental individuals, particularly on the ISRUC and FACED datasets (with 13.4% improvement in average MF1 on ISRUC and 19.5% improvement in average MF1 on FACED). For stability, when most Domain-II. methods simply manage to lower the forgetting rate of prior information, our approach is capable of absorbing new knowledge while further enhancing the model's generalization ability on the generalization set. It can be clearly observed from the comparison of AAA and AAFI metrics between Mo and MN (e.g., the AAA metric on the FACED dataset between My and Myy: 24.0 vs. 36.5).
3.2.2 COMPARISON WITH OTHER METHOSS
We have compared our method against several existing unsupervised domain learning (UDA). continual learning (CL) and unsupervised continual domain adaptation (UCDA) methods: MMD (Getton et al. 20066 is UDA method to match the Maximum Mean Discrepancy distance of feature distributions. TSTCC (Eldele et al., (021) can learn time-series representation from unlabeled data, making it suitable for EEG data. EWC (Kirkpatrick et al. 2017) and LwF (Li & Hoiem 2017) are both regularization- baased CL methods, applying regularizatio to prevent the crucial parameters.
UCL-GV (Taufique et al. (022) employs FIFO-based buffer and contrastive alignment strategies. ConDA (Taufique et al.] [2021 a strategy of selectively mixing samples from the incoming batch and buffer data. COTTA [Wang al. 2022 uses weight-averaged and augmentation averaged prediction and stochastically restore strategies. ReSNT (Duan et al. 2023) employ a dynamic memory evolution based replay method to continual decode EEG signals. We implemented these methods based on proposed UICL setting. In practice, the appearance of each new individual in the continual flow is entirely random, and we cannot determine the order in which they arrive. The difference in the order of continual individual domain adaptation could directly influence the model's learning trajectory. Therefore, to provide a statistical comparison, we evaluate the stability and robustness of each method under different orders of continual individual flow. Specifically, we maintained the consistent partitioning of training, incremental, and generalization sets, and only altered the input order of the continual individual flow for each methods by random shuffling, repeated five times in total. Here, we only report the Plasticity of M, state and the Stability of MN, state, since each method performs the same in the Mo state. The statistical results are shown in Tab. and Fig. 4 Compared with other methods, BrainUICL achieves the best plasticity and stability. Among the compared methods, UDA-based methods perform the worst. While they could achieve better P on ISRUC and FACED, they dramatically degrade the S. Additionally, on Physionet, both the SP degrades. The performance of CL-based models is slightly better than that of the UDA methods, indicating that continual learning has a greater impact on performance than unsupervised domain adaptation in our UICL setting. In most cases, UCDA-based methods outperform other methods, they can consider the continuously varying domains. However, UCDA-based methods still fail to achieve better plasticity while simultaneously maintaining the stability. As shown in Fig. 4 the trend of stability changes during each round updating can be visually observed. On the ISRUC and Physionet datasets, all the compared methods exhibit a decline in the AAA and AAFI curves, except for our method. On the FACED dataset, all the curves demonstrate fluctuating upward trend; nevertheless, BrainUICI. outperforms other methods in the later stages of continual learning. Furthermore, our AAA and AAF1 curves first exhibit smooth ascending trend and ultimately converge to stability. Moreover, it is worth noting that the confidence intervals of the curves also exhibit converging trend, with larger intervals at the beginning and ultimately converging smaller interval. To sum up, our BrainUICL demonstrates strong stability and robustness during long-term continual learning, effectively balaneing plasticity and stability when compared to other methods.
3.2.3 ABLATION STUDY
To investigate the effectiveness of DCB and CEA modules in BrainUICL, we conducted an ablation study. The ablated methods are as follows: Base: both DCB and CEA modules are removed; Base+CEA: only DCB module is removed; Base+DCB: only CEA module is removed. BrainUICL: the framework with all components. Here, we aslo only report the Plasticity of M; state and the Stability of MN, state, since each ablated method performs the same in the Mg state. The results shown in Tab. 4 and Fig. 5 Compared with the Base, both the CEA and DCB modules can
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Table 3: Performance comparison with existing UDA, CL and UCDA methods.
ISRUCC
FACED
Physionet
ACC
MF1
AAA
AAFI
ACC
MF1
AAA
AAH1
ACC
MF1
AAA
AAPI
UDA
MMD ISICC
68.6 68.0st
62.2.1.
68. Lanz
61.3g1
65.5an
55.0g17
36.511
38.7g0
20.7a1.1
30.5
30.8
2a.lans
307
44 64.5a09
44.9
657aa9
48.3ga
45.0g0
46.4gn
244
65.2.a.
684ana
66.1ans
97.5
59.3a14
33-4.a7
16.2
45.8.o1
46.3aa1
464.
TL.7go.1
67.0
68.1gns
59.9..
38.3go
30.8g4
34.7g4
32.0gn4
47.0gn8
45.9gn5
45.8g9.3
44.2-64
UC -GV
71.8.a3
66.4.a.3
70.7ana
68.5
38.8
34.8
34.3-6.
31.7an4
42.7an.4
41.5aa3
42.5aa2
42.0.c.4
UCDA
CoeIA
CoTTA
72.2
T1.6
67.6
66.4_0.3
68 68.2an3
70.6
64.7am3
68.5an.1
39.3am
38.1
35.5gt.
34.3
34.7
33.9.44
32.1an9
S1.1a1-1
47 47.4an
45.5am.1
44.4an2
66.32n5
d6.1ana
44.9
44.-5a
43.6-4.3
RcSNE
70.7.00
06.2_67
71.3g5
0944.6
37.2
W8.4g:
S1.1111
45.5400
44.5g00
40.0gn
44.7-63
RminU/CCI.
74.9
69.9
74.0.z
72.0-a11
40.3.:1
36...a
36.0,a5
33.9.u
48.4.33
47.5.a3
48.7.13
48.3,a2
AAF1
0.75
0.70
0.70
0.65
ISRUC
0.65
0.60
0.60
0.55
0.55
0.50
10
20
30
40
10
20
30
40
0.35
0.30
FACED
0.25
0.25
0.20
0
10
50
60
10
20
40
0.475
0.475
Phy sionsiee M1
0.450
0.450
0.425
20
40
10
50
50
BranUICL
ReSNT
COTTA
CouDA
UCL-GV
EWC
TSTCC
Figure 4: AAA and AAFI curves of the compared methods and the proposed Brain method. Each point denotes an individual from the continual individual flow, and the middle-line represents the mean value of the AAA and AAF1 metrics under different input orders, while the shaded areas indicate their 95% confidence intervals. Notably, all methods have five same input orders and these orders randomly different. Our BrainUICU demonstrates the best stability compared other methods, with a p-value of less than 0.001.
achieve better SP, demonstrating their effectiveness in the UICL setting. For plasticity, DCB module contributes more to our BrainUICL framework compared to CEA in most cases. Only on the average ACC on ISRUC, CEA performs slightly better than DCB (74.2% vs. 73.7%). It is reasonable that the objective of CEA is to prevent the model from overfitting to the newly added individuals, them. Interestingly, even though continuously
which could result in lower performance add penalty terms on incremental individuals, the model achieves better plasticity on them. This can be explained by the fact that if the model has overfitted to some outlier individuals without any constraints, the strong domain shift leads to difficulty for further continual individual domain adaptation. What's worse is that the model may fail to recover and deviate further and further away during the subsequent CL process. For instance, on Physionet, the stability curves of the Base model training. However, they consistently
even surpass those of the CEA and DCB at the beginning decline upon encountering outlier individuals and moreover, fail to recover through subsequent adaptation. For stability, the performances MN,,, indicating they make roughly equal contributions to the model's stability. Combined with DCB and CEA, BrainUICL outperforms the ablated methods in both plasticity and stability across three datasets. Furthermore, during long-term continual individual adaptation, method effectively enables the model to maintain stability even when encountering outliers. The detailed analysis of the impacts of outliers can be found in the Appendix. H
DCB and CEA are close at the final model state

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Table 4: Performance comparison with ablated methods.
ISRUC
FACED
Physionet
ACC
MP1
AAA
AAPI
ACC
MF1
AAA
AAPI
ACC:
AAA
AAFI
Base
73.3
08.5gn
732203
71:240.3
36221.1
31.8e14
32.6an
29.620
47.3e9.2
46.5aa.
47.8gns
47.2204
Base CEA
73.9
68.5ga1
73.5g0
71.640.
37.6213
33.9g1
34.3209
31.7x12
42.749.3
468808
48.0ga1
47.6-a
Boxe DCB
74.1
69.1g9
73.4g0
71.4203
374
33.0g11
33-424
30.4go
48.120
47.4g.
47.9g0
47.5go
BIiL
69.
74.0aa
a2.0.a1
36,0.as
484..3
47.5
48.7aat
483
AAA
AAFI
0.74
0.73
0.71
ISRUC
0.72
0.70
0.69
10
20
40
20
30
0.35
0.3
FACED
00,30
0.25
0.2
10
20
30
40
10
20
30
40
50
60
0.48
Physiiomst-Mi
0.48
0.47
0.47
BrainUICI
DCB
CEA
Basc
Figure 5: AAA and AAFI curves of the ablated methods. Each point denotes an individual from the continual individual flow with the middle-line indicating the mean value of the AAA and AAFI metrics under different input orders, while the shaded areas indicate their 95% confidence intervals. Notably, all methods share five same input orders and these orders are randomly different. The experimental results demonstrate the effectiveness of the proposed DCB and CEA components.
4 CONCLUSION
In this work, facing practical upplications. we try to make the model not only adapt well to continu- ously newly incoming subjects, but also generalize well to all the unseen subjects. We propose a novel UICL paradigm for handling EEG tasks in practical applications. And we propose the BrainUICI. framework to balance the plsttiity-sttabilig dilemma in this setup. The main objective of BrainUICL. to enable the model to continuously adapt well to multiple newly emerging subjects (better P) and simultaneously improve its generalization ability for all unseen subjects (better S), finally becoming universal expert. We effectively prevent the model from overfitting to incremental individuals during long-tem continual individual domain adaptation by increasing the penalty imposed them. The penalty consists of two parts. First, we employ a selected storage and real-pseudo mixed replay strategy improve the reliability of replayed EEG samples. Second, align the incremental model at different temporal states every two epochs to prevent overfitting the model to specific individual distributions. The effecctiveness of the proposed Brain has been evaluated on three different downstream EEG tasks. It enables continual individual domain adaptation applications that hold significance in practical setting.
5 ACKNOWLEDGMENTS
This work supported by STI 2030 Major Projects (2021ZD0200400), the Key Program of the Natural Science Foundation of Zhejiang Province, China (No. LZ24F020004) and the Natural Science Foundation of China (No. 61925603). The corresponding author is Dr. Sha Zhao.
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A
RELATED WORK
EEG Decoding, Recently, numerous deep learning-based models have been proposed for EEG tasks al., 2013; Pan et 2018 Qi et 2019 Zhang et 2019). For instance, Waag et al. (2024a); Zhou et al. (024b); Phan et al. (2021 Zhou et al. (2024a) employed GEG-based model sleep staging. replacing the need for manual scoring. Wang al. (2024b) 2023): Alturki al.) (2020) utilized HEG-basec model to assist in clinical disease diagnosis. Liu et al. (2023b a) are able to recognize subjects' emotions through EEG signals. However, they overlook the practical situations, as the parameters of these models typically remain fixed after training, leading to limited generalization ability and constraining their application in practice.
Continual Learning. Numerous CL methods have been developed to tackle the stability-plasticity (SP) dilemma. Research on continual learning can be categorized into three major streams. The regularization--asee methods: Kirkpatrick et al. (017)); Zenke al. (0077); AAljund (
al. (2018)
Li Hoiem (017)); Chaudhry et al. (2018b) directly apply regularization to the parameters to prevent
significant changes those crucial parameters. The parameter isolation based methods: Rusu
et al. (2016;; : Mallya & Lazebnik (018); Fernando et al. (2017) allocate different parameters to
different tasks prevent subsequent tasks from interfering with parameters learned previously. The Sthearsal-based methods: |Rebuffi et al. (2017); Castro et al. 20:18); Lopez-Paz & Ranzato (0017;;
Aljundi et al. (2099) alleviate catastrophic forgetting by replaying a subset past tasks from a stored memory buffer. Based on these classical CL methods, some of works like Wang et al. (2022); [Tang]
al. (20TT); Saporta al. (2022) focus on Continual Domain Adaptation( (CDA) problem, which shares the same setting ours. UCL-GV Taufique al. (2022) utilized contrastive loss to align the gap between the samples the existing buffer and the gradually varying target domain.
Continual EEG Decoding. Recently, existing studies have focused cross-subject continual EEG decoding. Duan et al. (2023) proposed a dynamic memory evolution based replay method to decode streaming EEG signals. Duan al. (2024b) proposed a bi-level mutual information maximization based meta optimizer to for sequential EEG classification. Duan et al. (2024a) employed balanced and informative memory buffer to address this continual EEG decoding challenge.
B PRETRAINED MODEL DETAILS
To fairly validate our BrainUICL framework different downstream EEG tasks, employ an identical model architecture consisting of three parts: a feature extractor, a feature encoder and a classifier. The feature extractor consists of multiple CNN blocks extract EEG features, each of which includes a CNN layer, a batch normalization (BN) layer, an activation layer, and a pooling layer(only the first and the fourth CNN layer include the pooling layer). The feature encoder contains multiple TransformerEncodee classifier composed of several fully connected layers. Notably, we only modified the parameters of the input and output layers to adapt to different EEG tasks. Further details are illustrated in Tab. g
layers to learn the temporal information from the EEG data. The
CNN Block
Layer
Classifier
+
Conv
Pooling
Drup
Encoder Layer
Outgut
FEG
GELU
BN
Softmax
Figure 6: The detailed pre- model architecture.
C CONTRASTIVE PREDICTIVE CODING
For each arrived individual, we employ guiding model to produce high-quality pseudo-labels for them, and the pseudo-labels are used for subsequent adaptation. Considering the sequential nature of EEG data, we use the Contrastive Predictive Coding (CPC) algorithm to perform sele-super fine- tuning on the guiding model, improving the quality of the generated pseudo-labels, Given the latent representation H (ho, h1, f2, ha, ... by, h++1, het2, heta) from the feature encoder, the objective
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of CPC is to use the preceding time steps Hig: to predict the subsequent time steps HISiSL, where t and L denote the predicted time step and the sequence length, respectively. Specifically, we employ transformer as autoregressive model to encode Hist into contextual vector ct. Subsequently, we establish a prediction task in which we utilize linear layers to predict the future EEG time steps, from h until hr, by leveraging the contextual vector Ct, such that 2++4A: = fa((2), where # denotes the predicted time steps for h4+k- Then, leverage contrastive loss update the network. The objective is to specifically align the guiding model with the distribution of the individual target domain. The loss function is as follows:
CCPC - E loo (
(8)
Ho. Enseh, exp((h) fa(c))))
C1
Autoregressive Model
C
ho
ixs
h2
kg
hr
Re+
h++3
Feature Encoder
Feature Extractor
EEG
Figure 7: The overview of Contrastive Predictive Coding.
Specifically, when an incremental individual arrives, we first conduct CPC algorithm on the guiding model Mg. which is copied from the lastest model M;-1, using the incremental individual's samples. After the adaptation, we use the fine-tuned M, to generate pseudo labels for subsequent training. Specifically, obtain the classification prediction probabilities(ie., after the softmax layer) for each sample by inputting the incremental individual samples into guiding model My. Then, we retain only the high-confidence pseudo-labels with prediction probabilities exceeding the threshold &1 for subsequent training. For the threshold {1, setting it too high may result in too few generated pseudo-labels, while setting too low can introduce additional low-quality pseudo-labels. To address this, we conducted parameter selection experiment to evaluate the impact of different thresholds on the performance of the generated pseudo-labels, ultimately setting E to 0.9.
D UNSUPERVISED INDIVIDUAL CONTINUAL LEARNING SETTING
The detailed process of the UICL is shown in Fig. 8 At the beginning, we initialize the incremental model Mg using the pretraining set. The incremental model then needs continuously adapt each unseen individual one by one. After each round of adaptation, we evaluate the model's stability and plasticity on the generalization set and the latest individual, respectively. For example, the initial model Mo needs to adapt to the first individual in the continual flow, resulting in the incremental model M1. We evaluate the stability of the current incremental model Mj on the generalization set and evaluate the plasticity of the Mi on the latest individual(i.e.. the first individual). After that, the incremental model M1 needs to adapt to the next individual, and so on.
In sections 4.2.2 and 4.2.3, we assessed the effectiveness of our method under varying input orders of the continual individual flow while maintaining a consistent dataset partition. To facilitate understanding, we provide simple illustrative example, shown in the Tab. 5
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Generaiizzion (ie, Unson Individaas))
Stabilrty Evaluation
Smbility Evaluation
Subblity Fizlustion
Stability Evaluation
Pretmining Set (le., Source Domait
Unseparvinol Individuul
Cinespervisal
Unsupervisad
Uneapereinn Inlividia Demain Adoptation
Ma
Dortain Adaptation
M
Domain Adopaation
Ma
Domain Adaptation
Ms
Planticity Evaluation
Plasticity Evalsotion
Evalsotion
Increteetaal Cuntinal Individual
Time
Figure 8: The process of the Unsupervised Individual Continual Learning.
Train Set
Generalization Set
Incremental Set (i.e., Continual Individual Flow)
Indivvidua
Order
1,
4, 5
10
Order 2
1, 2,
4, 5
8+99+6+7->1
Order 3
I, 2,
4, 5
9-38+6 10-9--6-8- 477
Order 4
1,
4, 1,2,
Order 5
5,
7 +8-
Table 5: Overview of Training and Incremental Orders. Here, the numbers denote the different individual IDs.
E DATA PREPARATION
A sleep dataset consisted of the three sub-groups. We specifically selected sub-group which consists of all-night polysomnography (PSG) recordings from 100 adult individuals and contains 86400 samples. We use six EEG channels (F3-A2, C3-A2, O1-A2, F4-AI, C4-A1, O2-A1) and two BOG channels (E1-M2, E2-MIJ, and the data is resampled to 100 Hz for evaluation. All EEG signals are divided into 30-second segments, which are then categorized into five distinct sleep stages (Wake, N1, N2, N3, REM) by sleep experts based on the standards set by the American Academy of Sleep Medicine (AASMIber (007)). The transition patterns between sleep epochs essential for sleep staging. In line with previous sleep staging studie<Phan & Mikkelsen (2022). we treat this task as a
ISRUC:
classification problem, defining the sequence length as 20, which corresponds to one sleep sequence consisting of 20 30-seconds samples. We excluded subject 8 and 40 due to some missing channels.
FACED: A large finer-grained affective computing EEG dataset covers nine emotion categories (amusement, inspiration. joy, tenderness, anger, fear, disgust, sadness, and neutral emotion) from recordings of 123 subjects. Each recording contains 32-channel EEG signals at 250 Hz sampling rate. All EEG signals are divided into 10-second segments. All the 123 recordings were used for evaluation.
Physionet-MI: A motor imagery EEG dataset covers four motor classes (left fist, right fist, both fists and both feet) from recordings of 109 subjects. Each recording contains 64-channel EEG signals at 160 Hz sampling rate. All BEG signals are divided into 4-second segments. All the 109 recordings were used for evaluation.
F HYPER-PARAMETED STUDY
F. DYNAMIC CONFIDENT BUFFER
In the Dynamic Confident Buffer. the buffer samples Ag selected from both Strue and Speeudo in an 8:2 radio. In this section, we conduct dyper-parameter study to validate the effectiveness of our settings shown in Tab.
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Published as a conference paper at ICLR 2025
In most cases, when the selected ratio is set to 8:2, the incremental model can achieve better SP. This suggests that the preference for replaying samples from the true-labeled storage Strue can lead to better review. The experimental results also indicate that replaying samples from the pseudo-labeled storage Spseudo in moderation can improve the performances, as it provides more diversity in replaying. In the FACED dataset, the model achieves better plasticity with ratio of 10:0 than with 8:2. However, it provides much poorer stability without replaying any pscudo-labeled samples from the incremental individuals (35.6% vs. 36.5% in AAA). Compared with the radio of 10:0 and 0:10, the latter performs much worse on both stability and plasticity. This indicates that relying entirely on replaying pseudo-labeled samples can introduce additional noise, leading to error accumulation and forgetting. To sum up, replay relatively more real samples from the training set to ensure the accuracy of the labels for the replay samples. Meanwhile, we replay a small amount of pseudo-labeled samples produced from the CL. process to increase the diversity of the replay samples.
Table 6: Analysis of the Strue- Selected Ratio in DCB.
ISRUC
FACED
Physionet
Strae' Symeaao
ACC
MF1
AAA
AAFI
ACC
MF1
AAA
AAF1
ACC
MF1
AAA
AAFI
0:10
72.6
67.1
72.6
70.6
38.5
34.3
34.3
31.7
47.0
46.1
48.0
47.4
2:8
72.2
66.9
72.8
70.8
39.0
35.3
35.0
32.6
47.9
47.0
48.4
47.9
5:5
74.1
69.0
73.2
71.2
39.3
35.7
35.1
32.7
48.0
47.1
48.6
48.2
8:2
75.1
70.0
74.1
72.1
40.3
37.1
36.5
34.5
48.2
47.4
48.8
48.5
10:0
74.3
69.1
73.8
71.8
40.8
37.4
35.6
33.5
48.2
47.3
48.7
48.4
F.2
CROSS EPOCH ALIGNMENT
In the CEA module, The alignment interval can be regarded as a tbyer-parameter parameter that controls the impact of the incremental individual the model. As the alignment interval decreases (e.g., from every two epochs to every epoch), the model performs the alignment operation with the previous model state more frequently. It means the penalty for the impact of incremental individuals is greater and the incremental model is less likely to be affected by new individuals. Meanwhile as the alignment interval increases (e.g., from every two epochs to every five epochs), the model performs fewer alignment operations, which increases the influence of incremental individuals on the model. To better verify the impact of different selections of the alignment interval, we conducted a hyper-parameter study.
Based on our setting, the training epoch of the fine-tune stage is set to 10. Therefore, we only test the alignment interval from (iie., every epoch) to 5(i.e., every 5 epochs) as the bigger interval is meaningless. The results show that in most cases, when we align with the previous model state every two epochs (i.e., align total of five times), the incremental model can better balance stability and plasticity. For instance, in FACED, when we perform alignment every 4 epochs, it can achieve better plasticity than every epochs. However, provides much poorer stability (35.6% and 33.3% vs. 34.5% in AAFI). ). To sum up, our proposed CEA module aligns the distribution the previous model states every mitigated by aligning with the distribution of earlier model states. This approach is beneficial as effectively prevents the model from overfitting to specific individuals, thereby avoiding a deviation from the original learning trajectory and ensuring the model stability during such long-term continual learning process.
36.5% in AAA
epochs. When the model begins overfit
individuals, this is
Table 7: Analysis the Alignment Interval in CEA.
ISRUC
FACED
Physionet
Alignment Interval
ACC
MF1
AAA
AAFI
ACC
MF1
AAA
AAFI
ACC
MF1
AAA
AAFI
Every Epoch
75.5
70,3
73.7
71.7
39.2
35.2
35.5
33.3
47.8
47.2
48.6
48.4
Every 2 Epochs
75.1
70.0
74.1
72.1
40.3
37.1
36.5
34.5
48.2
47.4
48.8
48.5
Every Epochs
74.9
70.2
73.6
71.6
40.3
36.5
35.7
33.3
48.0
47.1
48.7
48.5
Every Epochs
74.8
70.0
73.7
71.7
40.7
36.9
35.6
33.3
48.2
47.4
48.5
48.1
Every Epochs
74.4
69.9
73.8
71.8
39.3
35.7
35.1
32.7
48.2
47.2
48.6
48.2
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Published as a conference paper at ICLR 2025
The details of the experimental settings for BrainUICL framework are listed in Tab.
Table 8:
of the proposed BrainUICL. For Conv ID, the parameters from left to right are: (filter, kernel size, and stride).
Fpuch
Learning Rate
le-4
Pro training
Adam P1
AdauW Pg
0,39
0.5
AdamW Weight
3e-4
Ratch
1-th Convil
(64, S0, 6)
1-th MaxPoolLD
(8,81
CNN Blocks
Coav ID
ConviD
(128,
(256,
Couv
4-th Poodl
(4.44
Assemiion Head
Attcntion
Attention Layer
Dropout
0.1
supervised Leurning
Learning
Lppcc
1o-6
10
Epach
10
Continual Adapcation
Confdent Threshold
Learning Rate
Ie-7
0.9
Confident Thieshold
Aliguneut Interval
G COMPUTATIONAL COST
Physiond-Mi
Cal (ooolay
4.508u
13
20
20
40
50
23
66
40
Continual Individuull Flow
Coutimmal Indivichual Flow
Contimal Individual Flow
Figure The computational cost per individual.
To assess the computational efficiency of proposed BrainUICL. framework, conducted comprehensive analysis of the time cost per individual across three diverse datasets, as illustrated in Fig. 9 Our BrainUICL framework enables the model to rapidly adapt unseen individual, with an average processing time of just a few seconds. This rapid adaptation capability is a crucial feature of our BrainUICK framework, positioning an ideal solution for real-world applications that demand quick and seamless integration.
H IMPACT OF OUTIERS
We have listed the performance changes of partial outliers and their impact on the incremental model on the ISRUC dataset in Tab. 9 As we can see, the initial performance the model Mo on these outliers is quite low. Then, with the model continuously absorbs the new knowledge, before the adaptation M;-1, the performance of these outliers has already seen significant improvement compared to the initial state Mo. After adaptation, their performance is further enhanced M, Meanwhile, the model's generalization ability also steadily increasing, demonstrating that our method can not only improve the performance outliers, but also achieve better stability after each adaptation.
I PERFORMANCE VARIATIONS IN TRAIN SET
In this section, we evaluate the performance variations of the training set throughout the continual learning process, as illustrated in Fig. 10 The training set is used solely for pretraining the initial
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Published as a conference paper at ICLR 2025
Table 9: The performance changes of partial outliers and their impact the model the ISRUC
dataset. Here, ID denotes the position of outliers in the continuous individual flow. Mo denotes the initial model. M,- and M; denote the incremental model before and after adapting the current individual, respectively.
Evaluation of Plasticity
Evaluation Stability
Individual ACC
Individual MFI
AAA
AAFI
Outlier ID
Ma
M;-1
M.
Mo
M
M:
M;-1
M;
Mi-1
Mi
ID =6
35,93
46.51
53.37
19.62
36.01
48.96
72.87
73.08
70.91
71.12
ID = 10
28.87
45.75
60.85
9.92
38.59
52.78
73.23
73.29
71.24
71.32
ID 12
36.07
41.90
46.31
10.60
37.93
39.97
73.37
73.47
71.39
71.53
ID = 24
34.06
49.38
50.42
24.04
40.97
41.47
73.47
73.51
71.48
71.53
ID 25
37.86
52.74
63.33
10.98
46.53
52.90
73.51
73.56
71.53
71.59
ID
24.87
39.49
54.62
9.58
34.51
49.99
73.60)
73.62
71.63
71.64
ID =
20.83
55.42
56.25
14.52
47.97
51.81
73.88
73.90
71.90
71.91
ID 40
28.54
66.45
76.56
20.37
62.01
69.86
73.99
74.03
72.00
72.05
incremental model My and does not participate in the subsequent continual learning process. We do not analyze the results the Physionet-MI dataset, the initial model Mj has already demonstrated high performance on this dataset. In contrast, on the ISRUC and FACE datasets, the model's performance the training typically associated with continual learning. This is reasonable, given that 80% of the samples we replay are sourced from the training set, which enhances performance as we continuously replay the labeled samples from the training set.
shows overall improvement, rather than the catastrophic forgetting
ISRUC
FACED
Plysionet-MI
3.85
0.5
0.997
AAA
AAA
AAA
0.75
0.4
AAFI
0.996
AAFI
10
20
40
40
50
Figure 10: The performance variations of the train set during the continual learning.
J COMPARED WITH OTHER MEMORY SAMPLING METHODS
To validate the effectiveness the proposed DCB-based memory sampling approach, we conduct comparative study with other popular memory sampling methods: FIFO (i.c., First-In-First-Out RS (i.e., reservoir sampling), and Uniform (i.e., uniform random sampling). Notably, in this study, only replace DCB-based memory sampling method with the other methods, maintaining the consistency of other components to ensure fair comparison. Our method significantly outperforms the compared methods, as illustrated in Tab. 10 and Fig. I1 particularly the FACE and Physionet- MI datasets. Overall, the FIFO-based approach performs the worst, as it relies heavily on data from the previous individual for replay. When encountering outliers, the model's performance inevitably declines and may not be recoverable (see the FIFO curve in Physionet). On the Physionet dataset, the UCLGV method using the FIFO setting exhibits a similar downward trend, as shown in Fig. 4 Among the comparison methods, the uniform approach performs the best. This is because, although we save all newly added individual samples into storage, the number of true labeled samples from the training set remains significantly higher than that of pseudo-labeled samples during the early stages of training. Consequently, the randomly sampled replay samples are predominantly accurately labeled. However, in the later stages of training, pseudo-labele labeled samples continuously added to storage without filtering, each replay introduces a substantial number of low-quality samples, resulting in a decline in model performance (see the Uniform curve in ISRUC).
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Published as a conference paper at ICLR 2025
Table 10: Performance comparison with other memory sampling methods is presented. Notably, FIFO refers to First-n-FFrrt-Outt RS denotes reservoir sampling, and Uniform indicates uniform random sampling, respectively.
ISRUC
FACED
Physionet-Me MI
ACC
MF1
AAA
AAFI
ACC
MFI
AAA
AAFI
ACC
MFI
AAA
AAFI
FIFO
70.5
65.6
71.3
69.0
34,9
29.6
30.4
43.1
41.9
43.2
71.2
65.8
70.7
68.6
33.4
28.8
30.7
44.8
44.7
Uniform
74.2
68.7
73.4
71.4
37.8
33.3
33.1
47.3
47.7
(DCB)
75.1
70.0
74.1
72.1
40.3
37.1
36.5
34.5
48.2
47.4
ISRUC
FACED
Physionei-Mi
0.74
0.35
9.48
AAA
0.46
0.70
0.25
0.44
10
20
40
20
40
10
20
30
40
50
0.35
0.70
0.46
0.25
Ours
Unifemm
FIFO
Figure 11: AAA and AAFI curves of the compared memory sampling methods and our DCB method.
K PARTITION STUDY
In sections 4.2.2 and 4.2.3, assessed the effectiveness of method under varying input orders of the continual individual flow while maintaining a consistent dataset partition. To evaluate the effectiveness of proposed method across different dataset partitions, conducted a partition study. In this study, while keeping other experimental settings unchanged, we randomly shuffled the dataset partitions (i.e., pretraining set, incremental set, generalization set) for experimentation, repeating the process three times, as shown in Tab. [ and Fig. [2] The experimental results demonstrate that our method consistently exhibits strong performance across various dataset partitions, remaining unaffected by the specific partitioning of the dataset.
Table 11: Overview performance BrainUICL on three EEG tasks under different partition.
Evaluation of Plasticity
Evaluation Stabdity
Average ACC
Average MF1
AAA
AAFI
Mo
M,-1
M;
Mo
M;-1
M;
Mo
Myr
MMc
Myrr
Partition]
72.6
(46.9)
(+10.4)
(+4.5)
(+5.3)
Partition2
65.3
72.9
74.5 (+9.2)
57.8
67.1
69.6 (+11.8)
71.9
74.7 (+2.8)
69.0
72.0 (+3.0)
ISRUC'
Partition3
72.1
(+8.6)
72.5
76.6 (+4.1)
70.3
(+4.5)
Original
65.1
72.8
75.1 (+10.0)
57.6
67.1
70,0 (+13.4)
72.0
(+2.1)
69.9
72.1 (+2.2)
Partition
23.6
36.9
37.1 (+13.5)
16.9
33.1 (+16.2)
25.4
35.7 (+10.3)
20.8
(+12.4)
FACED
Partition2
23.8
38.6
39.2 (+15.4)
17.3
34.6
35.1 (+17.8)
24.9
37.1 (+12.2)
19.8
34.6 (+14.8)
Partition3
24.1
38.8
39.3 (+15.2)
17.5
35.5
35.7 (+18.2)
24.1
35.6 (+11.5)
18.3
(+14.9)
Original
24.2
38.9
40.3 (+16.1)
17.6
35.2
37.1 (+19.5)
24.0
36.5 (+12.5)
18.7
34.5 (+15.8)
Partition I
44.5
45.6
45.9 (+1.4)
43.0
44.5
44.9 (+1.9)
50.9
52.5 (+1.6)
50.4
$2.3 (+1.9)
Physionet-M1
Partition2
47.4
49.8
50.1
46.0
49.A (+3.4)
43.3
44.2
42.9
44,0
Partition3
45.6
46.7
48.2 (+2.6)
44.4
45.6
47.A (+3.0)
48.1
49,9 (+1.8)
47.6
49.6 (+2.0)
Original
46.1
47.4
48.2
44.7
46.3
47..77
46.9
48.8 (+1.9)
46.3
48.5 (+2.2)
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Published as a conference paper at ICLR 2025
0.74
1081C
00
28
26
18
50
11
39
TACID
0.53
21
11
30
AAA
AAFI
Figure 12: AAA and AAFI curves of the our methods with different dataset partitions.
L FUTURE WORK
In current practice, many EEG-related traditional manual assessments have been replaced by deep learning based models. These models are typically trained on source domain and then applied to practical testing. However, there are many limitations within this application. On the one hand, the model's generalization performance is limited due to constraints on the size of the source domain. On the other hand, the model with fixed parameters may not adapt to each unseen individual due individual discrepancies. To address this issue, proposed the BrainUICL framework which enables the EEG-based model to continuously adapt to newly appearing subjects, while simultaneously strengthening its generalization ability for those unseen subjects. On the downside, we have only applied our proposed BrainUICL framework to three mainstream EEG tasks (i.c. sleep staging, emotion recognition, and motor imagery). The primary reason the limited size of publicly available datasets for other EEG tasks, which typically only include a few dozen individuals at most. In future work, in addition to the aforementioned three EEG tasks, we intend extend our proposed BrainUICL framework to include broader range of practical EEG-based tasks (e.g., Major Depressive Disorder Diagnosis, Fatigue Detection, Disorders of Consciousness Diagnosis).
22


Reviews:
[{"review_id": "7NS5Utsgt6", "reviewer": "Reviewer_CyMS", "summary": "The author proposed to address the problem that EEG-based model trained on fixed datasets cannot generalize well to the continual flow of numerous unseen subjects in real-world scenarios. The authors propose BrainUICL which enables the EEG-based model to continuously adapt to the incoming new subjects, involving the Dynamic Confident Buffer (DCB) to selectively review the past knowledge and Cross Epoch Alignment (CEA) method to align the model at different time states.", "questions": "As listed in the strength and weaknesses sections above.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The work is tackling an important problem which potentially can have significant impact in real world. The manuscript is easy to follow in general and the method caters well to the problem settings.", "weaknesses": "It is recommended that the authors to test the model on a wider range of EEG datasets covering different tasks for evaluation of model effectiveness, such as DEAP and high gamma etc.\n\nDetailed analysis on memory cost is needed for the proposed operations such as the dynamic confident buffer and the cross epoch alignment. \n\nHow the different individuals are ordered during the continual learning process? Are they ordered by id or other attributes? Would different ordering affect the model performance much?\n\nRecent works that also cover the exact topic of continual learning on EEG signal are missing in related work section, such as [1][2][3].\n\nI would recommend a more modulized fomulation of related works, e.g. explictly divide the continual learning approaches into subsections   such as regularization, memory based approaches etc., and also distinguish between classic EEG decoding with continual EEG decoding for the EEG analysis part.\n\nGiven the work tackles specifically the EEG signal related task, better to highlight in introduction of the possible impact for the proposed continual EEG learning algorithm in real world applications.\n\nMore detailed explanation is needed for figures in the manuscript such as Fig. 3, 5 etc. \n\n\n[1] Online continual decoding of streaming EEG signal with a balanced and informative memory buffer, Neural Networks, 2024\n[2] Replay with Stochastic Neural Transformation for Online Continual EEG Classification, BIBM 2023\n[3] Retain and Adapt: Online Sequential EEG Classification with Subject Shift, IEEE Transactions on Artificial Intelligence, 2024", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "4VsEhnjP2P", "reviewer": "Reviewer_bzyV", "summary": "Individual differences are evident in EEG datasets, and the authors employed continuous learning to facilitate adaptive models for handling new subjects or patients.", "questions": "See above.", "limitations": "", "rating": 1, "confidence": 5, "soundness": 1, "presentation": 2, "contribution": 1, "strengths": "Authors tried to use continual learning to adaptively manage individual differences in EEG signals.", "weaknesses": "The claim regarding this study’s contribution is confusing, and the related work review is limited. Individual differences in EEG data are a well-known challenge, and substantial prior work in supervised learning and transfer learning has effectively addressed this issue using robust feature representations. There are many popular EEG datasets for classification tasks that were not discussed and considered.\n\nThe authors argue that existing EEG models lack practical applicability, especially in clinical settings with diverse patient profiles (refer to abstract). However, their selected EEG datasets do not include patient data, covering only sleep, emotion, and motor imagery tasks—none involving clinical data. Moreover, several widely-used EEG datasets for classification tasks are notably absent from their analysis.\n\nPrevious work on the datasets (above mentioned) they examined has achieved over 90% accuracy in classification tasks through supervised or transfer learning, which suggests these approaches can manage individual differences well. In contrast, this study reports accuracy levels around 40%, which raises the question: what factors account for this significant performance gap?\n\nThe role of cross-epoch alignment is unclear, particularly regarding its effectiveness in managing within- and across-subject variations. A more detailed explanation of its purpose and impact on these aspects is needed.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Z3rP85oYix", "reviewer": "Reviewer_BBh5", "summary": "The work proposes a Continual learning-based framework for addressing the need for robustness against user-specific variability in EEG-based BCIs. The model agnostic approach combines Unsupervised Domain adaptation with a Continual learning framework. 3 different tasks with public datasets are used for the benchmark. Evaluation metrics use incremental individual test sets to measure plasticity and a dataset for generalisation to measure the stability of the approach.", "questions": "Quoting the lines from authors: Plasticity (P) denotes the model’s adapting ability to newly emerging individuals, while Stability\n(S) indicates the model’s generalization ability to unseen individuals (i.e., new subjects)\nStability refers to the ability to maintain performance on previously seen and unseen individuals, including catastrophic forgetting. The current quote may lead to a misunderstanding. How well does it retain the performance on the dataset used for the M0 model?\n\nThe authors mention as follows:\nWe first explore the concept of Unsupervised Individual Continual Learning(UICL) in EEG-related applications, which is well-suited to the real-world scenario.\n\nIs the concept of UICL novel or has it been proposed earlier? It is not clear from the subsequent discussion in related works. How is it different from Unsupervised Domain Adaption and CL combination apart from defining an individual as a domain?\n\nThe concept of generating pseudo labels is not clear. Appendix B clarifies the SSL mechanism used for incremental subjects. However, post-training, how are the pseudo-label confidence values generated, and how is the confidence threshold decided is not clear.\n\n\nIn section 3.3.2, the authors mention: \"Here, we tend to utilize the real labeled samples for replay rather than the previously preserved\npseudo-labeled samples.\" Does this mean that the approach uses real labels for the selected pseudo-labeled samples?\n\n\nAlgorithm 1 on page 6 mentions Mg and Mi-1. However, while using DCB and CEA, Mg is not used and instead, Mi-1 is used. At the same time, the text mentions the use of CPC for adapting to the user's domain. Can the authors clarify this?\n\nThe authors do not mention the data preparation step for each dataset, i.e. how long the epochs are, any overlaps between the epochs, and details on the block sizes of the CNN. Some of these parameter choices are significant in evaluating the effectiveness and explainability of the approach.\n\nThe results reported in Table 3 and Figure 4 caption mention: Notably, all methods have five same input orders, and these orders are randomly different. It is unclear if the individuals added to the model are in the same order for each iteration. And are they shuffled randomly across those five iterations? I assume that the 95% CIs and SDs in Table 3 are coming from these 5 iterations of different orders. \n\nAre the ACC and MF1 values averaged across incremental individuals with models Mi and across the five iterations of the order? The results are not clear after reading through the sections and looking at tabular data. \n\n\nIn Table 4, Figure 5, ablation results, it is surprising that the base performance(AAA and AAF1) does not decline with the addition of individuals. Does the base model have any replay? It would be good if authors could point to the section if already addressed.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The work addresses the domain's appropriate needs in terms of user variability. The approach is well proposed and benchmarked, including metrics compared with relevant SOTA, ablation studies and computational costs.\nThe work is technically detailed with appendices and presented with fair clarity.", "weaknesses": "The method section could be better represented with additional labels to the stages in Figure 2 that include the three stages explained in the overview: 1) producing pseudo labels, 2) updating models, and 3) updating storage. It wasn't easy to follow the complete process, shifting across figures, the overview section, each BrainUICL subsection and the appendix.\n\nIt's not a weakness per se. While the work is novel in its approach, authors can be more specific in contributions about the novelty of the approach across application domains. It is understood that the approach combines previously known approaches in Unsupervised domain adaptation and continual learning with novelty to the strategies in updating the replay buffer and the training loss, including cross-epoch alignment, where the motivation is similar to EwC.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "YlsHivXbMb", "reviewer": "Reviewer_J7FP", "summary": "Pre-trained EEG models often cannot be effectively generalized in practice due to high inter-subject variability. In this work, a novel unsupervised continual learning (CL) approach is proposed that aims to balance adaptation and generalization. To mitigate catastrophic forgetting, the method introduces a penalty term based on cross-epoch alignment and uses a dynamic confident buffer to preserve prior knowledge. Experiments conducted on three different datasets demonstrate superior performance.", "questions": "1.\tClarification is needed on how the threshold for self-supervised learning (SSL) is determined in the presence of inter-subject data heterogeneity. How effective are the generated pseudo-labels given this variability? Are there specific criteria for setting this threshold? Additionally, considering that the previous model may be biased toward earlier subjects, could inter-subject variability lead to inaccuracies in the pseudo-labels?\n2.\tHow is the plasticity of the incremental set evaluated? Is there a specific incremental split for training and testing?\n3.\tWhat is the total number of samples stored in the storage buffer for each individual? In addition, how are the samples of the target domain replaced in the memory?", "limitations": "", "rating": 5, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The research question addressed in the paper is interesting. The results show that the proposed approach outperforms existing methods.", "weaknesses": "1.\tThe selection mechanism for the buffer samples requires further clarification, particularly with regard to the number of samples retained per individual. To strengthen the evaluation, it would be helpful to compare the effectiveness of the proposed approach with standard memory sampling techniques, such as reservoir sampling, as well as recent advanced methods specifically designed to address inter-subject variability in EEG data\n\n2.\tThe KL-based penalty term needs further clarification, in particular why it is only applied in every second epoch and not in every training epoch. Furthermore, the mechanism that controls the impact of this penalty term remains unclear. Is there a specific parameter that controls this loss term to regulate its influence during training?\n\n3.\tHow the datasets are divided into source, target and test sets is unclear. Given the heterogeneity caused by inter-subject variability, if subjects were randomly assigned to each set (source, target, test), conducting the experiments in multiple runs and reporting the averaged accuracy would be advantageous.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)