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This paper introduces the Universal Semantic EEG Foundation Model (USE-FM), a lightweight, self-supervised model for EEG analysis that achieves comparable cross-disorder transfer performance to much larger models. By freezing the encoder after pretraining on the TUEG corpus, USE-FM demonstrates competitive sensitivity and F1-scores on epileptic seizure recognition (TUEP) and AUC on abnormal EEG detection (TUAB) with only 1.46 million parameters. Latent representation analysis confirms that USE-FM learns organized semantic EEG representations, suggesting that efficient foundation models are feasible for clinical decision support.
A 1.46M parameter EEG foundation model matches larger counterparts in cross-disorder transfer, unlocking efficient clinical AI.
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.