对比三种时间特征提取方法，发现预训练时序模型可直接用于脑电基础模型。
Temporal Feature Extractors in EEG Foundation Models: A Controlled Comparison Including a Pretrained Time-Series Model
- 测试线性、卷积和预训练时序模型三种时间特征提取策略
- 运动想象任务中简单特征已足够，情绪识别需更丰富时序建模
- 无需微调的预训练时序模型能有效提升脑电表征质量
脑电图（EEG）基础模型旨在从大规模脑记录中学习通用表征。然而，时间特征提取器的作用以及预训练时序基础模型（TSFM）是否可有效迁移至该场景仍不明确。我们在统一的EEG基础模型中，对三种时间特征提取策略进行受控比较：线性基线、卷积编码器与冻结的预训练TSFM（MOMENT）。通过运动想象和情绪识别两个下游任务评估其表征质量。结果表明，在不同基准上呈现不同趋势：在运动想象数据集上，简单的时间表示已具竞争力；而在情绪数据集上，更丰富的时序建模带来显著提升。尽管未针对EEG进行专门适配，预训练的TSFM仍可作为有效的时序特征提取器，表明通用时序表征可在冻结状态下迁移至EEG基础模型。
原文摘要 · Abstract (English)
Electroencephalography (EEG) foundation models aim to learn generalizable representations from large-scale brain recordings. However, the role of temporal feature extractors and whether pretrained time-series foundation models (TSFMs) can be effectively transferred to this setting remains underexplored. We conduct a controlled comparison of three temporal feature extraction strategies, including a linear baseline, a convolutional encoder, and a frozen pretrained TSFM (MOMENT), within a unified EEG foundation model. We evaluate their impact on representation quality using two downstream tasks: motor imagery and emotion recognition. Results reveal different trends across the evaluated benchmarks. On the motor imagery dataset, simple temporal representations perform competitively, whereas the emotion dataset benefits from richer temporal modeling. Although not specifically adapted to EEG, the pretrained TSFM serves as an effective temporal feature extractor, suggesting that general-purpose time-series representations can be transferred as frozen temporal feature extractors within EEG foundation models.
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