Lightweight Semantic EEG Foundation Model for Frozen Cross-Disorder Transfer

Rita Huan-Ting Peng; Nhat Bui

cs.LGOct 4, 2026

Lightweight Semantic EEG Foundation Model for Frozen Cross-Disorder Transfer

Authors: Rita Huan-Ting Peng, Nhat Bui

Organizations: University of Illinois Urbana–Champaign, Urbana, IL, USA · Carle Foundation Hospital, Urbana, IL, USA

Abstract

Large-scale EEG foundation models have demonstrated promising transferability across neurological disorders, but often require millions of parameters and substantial computational resources. In this paper, we present the Universal Semantic EEG Foundation Model (USE-FM), a lightweight EEG foundation model that learns transferable neural representations through self-supervised signal reconstruction on the Temple University Hospital EEG Corpus (TUEG). After pretraining, the encoder is frozen and evaluated on two clinically distinct downstream tasks, abnormal EEG detection (TUAB) and epileptic seizure recognition (TUEP), using a unified frozen-transfer protocol against recent EEG foundation models, including LUNA-Base and CBraMod. With only 1.46 million parameters, approximately one-fifth the size of existing models, USE-FM achieves competitive overall performance, including strong sensitivity and F1-score on TUEP (SEN $75.00 \pm 14.14$ , F1 $70.37 \pm 4.01$ ), while maintaining competitive performance on TUAB (AUC $85.24 \pm 5.61$ ). Beyond downstream classification, latent representation analysis using $k$ -means clustering together with PCA and t-SNE demonstrates that USE-FM learns organized semantic EEG representations comparable to substantially larger foundation models. These results suggest that large-scale self-supervised pretraining enables lightweight architectures to learn transferable semantic EEG representations, providing a computationally efficient foundation for cross-disorder analysis and future clinical decision support in neurological disorders.

Figures & tables

Fig. 1 : Overview of the proposed USE-FM. TUEG is used for large-scale self-supervised pretraining through EEG signal reconstruction with an MSE loss. The pretrained encoder is then frozen and evaluated on TUAB and TUEP. The learned latent representations are further analyzed using $k$ -means clustering, PCA, and t-SNE on TUEP.

Model	Parameters	Backbone	Latent Dim.
USE-FM	1.46M	CNN–Transformer	128
CBraMod	5.15M	Criss-Cross Transformer	200
LUNA-Base	7.10M	Rotary Transformer	256

TABLE 1 : Comparison of the evaluated EEG foundation models.

Panel of Figure 3 — Fig. 2 : Frozen-encoder transfer performance on (a) TUAB and (b) TUEP. Error bars indicate one standard deviation across independent runs. USE-FM is shown in light blue, LUNA-Base in light gray, and CBraMod in soft green.

Dataset	Metric	USE-FM	LUNA-Base	CBraMod
TUAB	AUC	85.24 $\pm$ 5.61	84.13 $\pm$ 3.75	84.09 $\pm$ 5.39
	ACC	80.25 $\pm$ 5.91	80.22 $\pm$ 4.21	80.23 $\pm$ 6.14
	SEN	81.13 $\pm$ 11.76	82.25 $\pm$ 12.49	80.99 $\pm$ 14.76
	F1	82.52 $\pm$ 5.79	82.53 $\pm$ 5.00	82.04 $\pm$ 7.12
TUEP	AUC	67.35 $\pm$ 5.91	67.00 $\pm$ 4.45	68.20 $\pm$ 8.60
	ACC	69.00 $\pm$ 3.24	67.50 $\pm$ 2.74	70.50 $\pm$ 6.78

TABLE 2 : Cross-disorder frozen-encoder classification performance (mean

\pm

std). The best result for each metric is highlighted in blue bold , and the second-best result is underlined .

Fig. 3 : WCSS for USE-FM, LUNA-Base, and CBraMod on TUEP. All models exhibit a similar elbow at $k=4$ , which was selected for latent-space analysis.

Fig. 4 : PCA visualization of the latent representations learned by USE-FM , LUNA-Base , and CBraMod using the $k=4$ clustering solution determined by the WCSS elbow criterion. Points are colored according to their assigned latent states. USE-FM exhibits a well-structured latent organization comparable to larger pretrained EEG foundation models while requiring substantially fewer parameters.

Fig. 5 : t-SNE visualization of the latent manifolds learned by USE-FM , LUNA-Base , and CBraMod . Colors denote the same four latent states shown in Fig. 4 . Compared with the baselines, USE-FM exhibits four latent states distributed across distinct and continuous regions of the learned manifold.

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EEG-JEPA: Structured Latent Prediction for EEG Foundation Models

Jinhao Li, Zhiyuan Ma, Xueqiao Han, Zhongye Xia+7

Tsinghua Laboratory of Brain and Intelligence, Tsinghua University · School of Basic Medical Sciences, Tsinghua Medicine, Tsinghua University · School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University+2

Electroencephalography Foundation Models Electroencephalography Latent Prediction Electrocardiogram Foundation Models Pretraining

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Electroencephalography (EEG) foundation models aim to learn reusable representations from large-scale unlabeled recordings. A common pretraining strategy is masked waveform reconstruction, but applying supervision directly to noisy EEG may encourage models to recover predictable background activity, acquisition effects, and artifacts rather than neural structure that transfers across tasks. This raises a central question: what should an EEG foundation model predict to learn transferable representations? We introduce EEG-JEPA a structured latent-prediction framework for EEG foundation modeling. Rather than reconstructing masked voltage samples, a masked context encoder and predictor infer contextual latent states produced by an exponential-moving-average target encoder that observes the complete input. EEG-JEPA organizes target design along three complementary dimensions: target content specifies what representation is predicted, target support specifies where prediction occurs over structured electrode--time regions through Neurotopology-Aware Multi-scale Electrode-Temporal Masking (N-MET), and target depth specifies at which encoder layers supervision is applied. Together, these designs shift EEG pretraining from recovering missing measurements to inferring latent states from structured electrode--time context. We evaluate EEG-JEPA through controlled objective comparisons, frozen multitask transfer, and full fine-tuning. Under the same backbone, pretraining corpus, and training duration, EEG-JEPA improves the 14-task frozen macro balanced accuracy from 40.49% to 50.42% over CBraMod-style masked waveform reconstruction. Multi-source continuation further raises this result to 52.94%, the highest average among the EEG foundation models evaluated on EEG-FM-Bench. Under protocol-matched full fine-tuning, EEG-JEPA also improves the nine-task average balanced accuracy from 68.98% to 70.65%.

Abstract

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