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.

翻译：时间序列基础模型正成为强大的通用骨干网络，但其在生物医学领域特定信号（如脑电图，EEG）中的应用潜力仍相对未被充分探索。本研究探讨了一种近期提出的时间序列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案：（a）在多领域异构真实世界时间序列上进行预训练，以及（b）在纯合成数据上进行预训练。研究发现，两种方案均表现出强劲性能，持续超越广泛使用的卷积基线模型EEGNet以及最新的EEG专用基础模型CBraMod。这些结果表明，通用型时间序列基础模型即使基于非神经源数据或合成信号进行预训练，也能有效迁移至EEG分析。我们的发现凸显了利用跨领域预训练模型进行脑信号分析的前景，表明EEG研究可能受益于更广泛时间序列文献的进展。