通用时间序列模型可有效用于脑电分类，无需专用数据预训练
Leveraging Generic Time Series Foundation Models for EEG Classification
- 用跨领域真实数据或合成数据预训练通用时间序列模型
- 在运动想象与睡眠分期任务上超越现有基线模型
- 适合关注脑信号分析的交叉学科研究者
时间序列基础模型正成为强大的通用骨干，但其在脑电图（EEG）等特定生物医学信号中的潜力仍不明确。本文研究了一种近期提出的时序分类基础模型在不同EEG任务（如运动想象分类和睡眠阶段预测）中的适用性。我们测试了两种预训练方式：(a) 在多领域异构真实时间序列上预训练，(b) 在纯合成数据上预训练。结果表明，两种方案均表现优异，持续优于广泛使用的卷积基线模型EEGNet和最新EEG专用基础模型CBraMod。这说明即使在非神经数据或合成信号上预训练的通用时间序列基础模型，也能有效迁移到EEG任务。研究揭示了利用跨领域预训练模型进行脑信号分析的巨大潜力，表明EEG可从更广泛的时间序列研究进展中获益。
原文摘要 · Abstract (English)
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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