Leveraging Generic Time Series Foundation Models for EEG Classification

• URL: http://arxiv.org/abs/2510.27522v1
• Date: Fri, 31 Oct 2025 14:49:23 GMT
• Title: Leveraging Generic Time Series Foundation Models for EEG Classification
• Authors: Théo Gnassounou, Yessin Moakher, Shifeng Xie, Vasilii Feofanov, Ievgen Redko,
• Abstract summary: We investigate the applicability of a recently proposed time series classification foundation model, to a different EEG tasks such as motor imagery classification and sleep stage prediction.<n>We find that both variants yield strong performance, consistently outperforming EEGNet, a widely used convolutional baseline, and CBraMod, the most recent EEG-specific foundation model.<n>Our findings highlight the promise of leveraging cross-domain pretrained models for brain signal analysis, suggesting that EEG may benefit from advances in the broader time series literature.
• Score: 8.938023803309097
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Foundation models for time series are emerging as powerful general-purpose backbones, yet their potential for domain-specific biomedical signals such as electroencephalography (EEG) remains rather unexplored. In this work, we investigate the applicability a recently proposed time series classification foundation model, to a different EEG tasks such as motor imagery classification and sleep stage prediction. We test two pretraining regimes: (a) pretraining on heterogeneous real-world time series from multiple domains, and (b) pretraining on purely synthetic data. We find that both variants yield strong performance, consistently outperforming EEGNet, a widely used convolutional baseline, and CBraMod, the most recent EEG-specific foundation model. These results suggest that generalist time series foundation models, even when pretrained on data of non-neural origin or on synthetic signals, can transfer effectively to EEG. Our findings highlight the promise of leveraging cross-domain pretrained models for brain signal analysis, suggesting that EEG may benefit from advances in the broader time series literature.

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