Leveraging Generic Time Series Foundation Models for EEG Classification

📝 Original Info

• Title: Leveraging Generic Time Series Foundation Models for EEG Classification
• ArXiv ID: 2510.27522
• Date: 2025-10-31
• Authors: 논문에 명시된 저자 정보가 제공되지 않았습니다. (저자명 및 소속은 원문을 확인해 주세요.)

📝 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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