Brain foundation model-guided source-selective domain adaptation for cross-subject EEG decoding

Jinzhou Wu; Baoping Tang; Qikang Li; Yi Wang; Cheng Li; Shujian Yu

cs.LGJul 28, 2025

Brain foundation model-guided source-selective domain adaptation for cross-subject EEG decoding

Authors: Jinzhou Wu, Baoping Tang, Qikang Li, Yi Wang, Cheng Li, Shujian Yu

Organizations: State Key Laboratory of Mechanical Transmission, College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China · Department of Computer Science, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands · Machine Learning Group, UiT–The Arctic University of Norway, 9019 Tromsø, Norway

Abstract

Cross-subject motor-imagery electroencephalography (MI-EEG) decoding remains challenging because substantial inter-subject variability can cause both negative transfer from poorly matched source subjects and persistent distribution discrepancies between source and target domains. Existing multi-source domain adaptation methods often incorporate all available source domains or estimate source relevance using signal-level or task-specific representations, while distribution alignment is frequently performed only at the feature level. These limitations may introduce irrelevant source knowledge and fail to preserve class-discriminative structures across subjects. In this study, we propose a brain foundation model-guided multi-source domain adaptation framework (BFM-MSDA) for cross-subject MI-EEG decoding. The method first utilizes representations learned by a pretrained brain foundation model to estimate source--target compatibility and retrieve target-relevant sources. Subsequently, a relevance-weighted dual alignment strategy is applied to the selected sources and the unlabeled target domain. Specifically, Cauchy--Schwarz (CS) divergence is used to reduce discrepancies in marginal feature distributions, while conditional Cauchy--Schwarz (CCS) divergence further aligns class-dependent decision distributions. Source relevance is incorporated into both alignment terms so that more transferable source subjects contribute more strongly to adaptation. Experiments on two public MI-EEG benchmarks achieve average accuracies of 86.16% and 78.41%, outperforming representative cross-subject decoding and domain adaptation methods. Additional experiments with a large source pool further demonstrate that target-aware source retrieval improves scalability while mitigating negative transfer. These results highlight the importance of informed source selection under cross-subject distribution shift.

Figures & tables

Figure 1: Overview of the proposed BFM-MSDA framework for subject-independent MI decoding: (a) source selection with a pretrained BFM, where trial-level embeddings $\mathbf{h}_{1},\ldots,\mathbf{h}_{S}$ and $\mathbf{h}_{T}$ yield pairwise CS distances $d_{S_{i},T}$ used to retain target-relevant sources; (b) MSDA with feature-level alignment (FLA) via weighted CS on $p(z)$ and decision-level alignment (DLA) via conditional CS on $p(y|z)$ over selected source–target and source–source pairs; (c) testing, where the trained feature extractor and classifier predict MI labels.

Methods	Subject									Avg. acc.	Kappa
Methods	1	2	3	4	5	6	7	8	9	Avg. acc.	Kappa
EEGNet	72.92	64.58	79.17	78.47	86.11	74.31	83.33	79.86	82.64	77.93 ∗∗ $\pm$ 6.52	0.5586
ShallowConvNet	75.73	74.89	78.47	76.39	81.25	85.43	87.50	83.33	76.39	79.93 ∗∗ $\pm$ 4.62	0.5986
Conformer	81.59	68.53	88.07	82.04	74.68	78.54	86.97	82.59	83.74	80.75 ∗∗ $\pm$ 6.10	0.6150
JAN	79.69	76.32	84.25	75.69	86.81	84.72	89.19	90.28	81.94	83.21 ∗ $\pm$ 5.25	0.6642
DJP-MMD	79.78	72.22	89.58	81.94	85.42	83.33	88.89	89.58	86.28	84.11 ∗ $\pm$ 5.64	0.6823

Table 1: Cross-subject accuracies (mean

\pm

std. in %) and kappa on Dataset I. Best performances are highlighted in bold. ∗ represents

p<0.05

and ∗∗ represents

p<0.01

Methods	Subject										Avg. acc.	Kappa
Methods	1	2	3	4	5	6	7	8	9	10	Avg. acc.	Kappa
EEGNet	66.57	60.96	82.70	70.14	73.89	59.05	55.68	63.56	88.29	65.06	68.59 ∗∗ $\pm$ 10.41	0.3718
ShallowConvNet	73.92	67.63	75.05	71.55	75.23	63.65	70.56	67.70	78.02	66.37	70.97 ∗∗ $\pm$ 4.60	0.4194
Conformer	77.62	71.01	78.80	74.12	78.99	66.83	74.09	71.09	81.92	69.69	74.42 ∗∗ $\pm$ 4.82	0.4883
JAN	78.27	71.38	80.72	73.75	79.46	76.84	73.05	70.30	87.35	70.28	76.14 ∗∗ $\pm$ 5.47	0.5228
DJP-MMD	76.31	74.43	78.26	71.85	73.62	77.94	70.22	71.43	82.45	71.52	74.80 ∗∗ $\pm$ 3.89	0.4961

Table 2: Cross-subject accuracies (mean

\pm

std. in %) and kappa on Dataset II. Best performances are highlighted in bold. ∗ represents

p<0.05

and ∗∗ represents

p<0.01

Group	Subject										Avg. acc.
Group	1	2	3	4	5	6	7	8	9	10	Avg. acc.
Random Group 1	66.42	63.57	80.65	66.50	79.51	67.83	59.52	66.36	89.52	56.23	69.61 ∗∗ $\pm$ 10.37
Random Group 2	73.85	74.31	79.27	73.03	75.65	70.95	71.18	73.54	85.42	74.85	75.21 ∗ $\pm$ 4.29
Random Group 3	72.38	70.15	71.33	72.27	72.92	73.65	78.63	71.81	88.71	74.75	74.66 ∗ $\pm$ 5.45
Random Group 4	71.38	72.81	79.52	65.15	78.81	71.57	69.82	70.55	89.17	65.67	73.44 ∗∗ $\pm$ 7.24
Random Group 5	72.94	67.35	73.17	67.24	75.64	68.32	70.72	74.36	88.46	66.75	72.50 ∗∗ $\pm$ 6.47

Table 3: Cross-subject performances of different source groups from the GigaDB dataset. Best performances are highlighted in bold. ∗ represents

p<0.05

and ∗∗ represents

p<0.01

Figure 2: Effect of selection threshold on performance and training time.

Dataset	Mean Spearman $\bar{\rho}$	$p$ -value	Targets
Dataset I	$-0.304$	$0.016$	9
Dataset II	$-0.265$	$0.018$	10

Table 4: Mean within-target Spearman correlation

\bar{\rho}

between BFM distance and single-source transferability.

\bar{\rho}

is the average of the per-target rank correlations

\rho_{t}

. The

p

-value is from a blocked permutation test of

\bar{\rho}

Panel of Figure 3 — Figure 3: BFM distance against single-source transfer accuracy after centring accuracy within each target. Each point represents an ordered source–target pair, and the solid line is a least-squares fit. Larger BFM distance is associated with lower transferability on both (a) Dataset I and (b) Dataset II.

Panel of Figure 4 — Figure 4: Cross-subject classification accuracies of ablation variants. (a) Dataset I. (b) Dataset II.

Dataset	Baseline	CS/CCS	KDE-KL
Dataset I	77.93 $\pm$ 6.52	86.16 $\pm$ 5.37	79.34 $\pm$ 8.46
Dataset II	68.59 $\pm$ 10.41	78.41 $\pm$ 6.57	71.37 $\pm$ 9.24

Table 5: Cross-subject performance comparison between CS and KDE-based KL divergences.

Panel of Figure 5 — Figure 5: Heat maps and MDS visualizations of pairwise distances for Datasets I and II. (a) Heat map of inter-subject distances for Dataset I. (b) MDS visualization of inter-subject distances for Dataset I. (c) Heat map of inter-subject distances for Dataset II. (d) MDS visualization of inter-subject distances for Dataset II.

Figure 6: t-SNE visualization of target domain features extracted by the feature extractor for subject 1 from Dataset I (top row) and Dataset II (bottom row). (a) and (d) baseline EEGNet without DA, (b) and (e) MSDA without source selection, and (c) and (f) full BFM-MSDA framework.

Panel of Figure 7 — Figure 7: EEG topography for subject 1 from Dataset I with different MI tasks. (a) Raw EEG signals averaged across all trials. (b) Grad-CAM of extracted features after BFM-MSDA.

Panel of Figure 8 — Figure 8: Parameter sensitivity analysis. (a) Influence of balancing factors for Dataset I. (b) Influence of balancing factors for Dataset II. (c) Influence of transition point $\tau_{0}$ for Dataset I. (d) Influence of transition point $\tau_{0}$ for Dataset II.

Appendix figures & tables5 assets

Supplementary material from the paper’s appendix.

Appendix

Dataset	Analysis	$\bar{\rho}$	$p$	Blocks
I	Within source	$-0.283$	$0.026$	9
I	Pooled (72 pairs)	$-0.350$	$0.002$	–
II	Within source	$-0.132$	$0.250$	10
II	Pooled (90 pairs)	$-0.182$	$0.088$	–

Appendix

Table C.1: Supplementary rank correlations between BFM distance and single-source transferability under alternative pair groupings. Negative values indicate that nearer sources transfer better.

Figure D.1: Target accuracy against the number of selected sources on Dataset I, without domain adaptation. BFM-guided selection is above random selection at every

k

below the full pool and peaks at

k=6

. The dotted line marks the best accuracy random selection attains at any

k

. Error bars are the standard error across targets.

Figure D.2: Target accuracy against the number of selected sources on Dataset II, without domain adaptation. Accuracy falls

4.3

points from its maximum at

k=6

k=9

. Error bars are the standard error across targets.

Target	LaBraM-based selection		CBraMod-based selection		Overlap
	Selected sources	Acc. (%)	Selected sources	Acc. (%)	(%)
S1	S3, S4, S7	83.19	S3, S4, S8	84.27	50.0
S2	S1, S4, S6, S8	76.53	S1, S3, S5, S8	74.32	33.3
S3	S2, S6, S8	91.74	S4, S6, S8	89.08	50.0
S4	S1, S5, S8	83.26	S1, S3, S5, S6	82.15	40.0
S5	S3, S4, S7	89.37	S4, S7, S9	86.94	50.0

Appendix

Table F.1: Adaptive soft-gating source sets and downstream BFM-MSDA accuracies on Dataset I when source–target distances come from LaBraM or from CBraMod. Overlap is the Jaccard index. LaBraM accuracies match Table 1 .

Figure F.1: Inter-subject CS distances on Dataset I. (a) CBraMod embeddings. (b) LaBraM embeddings.

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Cross-Subject EEG Decoding Source-Free Domain Adaptation EEG Decoding EEG Foundation Models

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Source-free domain adaptation (SFDA) provides a practical solution to cross-subject EEG decoding by adapting source-pretrained models to unlabeled target domains without accessing source data. However, existing SFDA methods rely solely on the limited internal knowledge of source-pretrained models, leading to inferior cross-domain generalization and unreliable pseudo-labels. Although EEG Foundation Models (FMs) pretrained on large-scale data exhibit strong generalizability, their potential in SFDA remains largely unexplored. To this end, we propose FUSED, a Foundation-guided Source-free EEG Decoding framework that integrates a large-scale FM with a compact Specialist Model (SM) via dual-branch co-adaptation. Specifically, we introduce a Co-adaptation mechanism equipping both branches with linear and prototype views, enabling cross-branch pseudo-label generation. Additionally, we design a Consensus Filtering Mechanism that exploits the FM's inherent stability to identify high-quality samples, along with a Two-Stage Pseudo-Label Refinement scheme to suppress error accumulation through cross-branch arbitration. Finally, we calibrate the FM's decision boundaries via mutual information maximization with the SM, followed by knowledge distillation from FM to SM, forming a principled calibrate-then-distill pipeline. To our knowledge, FUSED is the first work to leverage EEG FMs within the SFDA framework for cross-subject EEG decoding. Extensive experiments across three EEG paradigms, including motor imagery, emotion recognition, and SSVEP, demonstrate consistent state-of-the-art performance, validating the effectiveness of foundation-guided synergy for robust and privacy-preserving EEG decoding.

Abstract

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