针对干电极脑电易受干扰的问题，提出融合时空与多尺度频域特征的新模型。
Fusion of Spatio-Temporal and Multi-Scale Frequency Features for Dry Electrodes MI-EEG Decoding
- 分三路建模：双图结构捕捉时空关联，多尺度频混分支提取稳定包络特征
- 在自建干电极数据集上显著优于CNN、Transformer等基线模型
- 适合可穿戴脑机接口场景，对电极接触变化不敏感，适合实际应用
干电极运动想象脑电（MI-EEG）通过去除凝胶和缩短准备时间，实现快速、舒适、适用于家庭和可穿戴设备的脑机接口。然而，干电极记录存在三大问题：信噪比低，伴有基线漂移和突发瞬态；数据较弱且噪声大，跨试次相位对齐差；会话间方差更大。这些导致数据分布偏移加剧，使特征在MI-EEG任务中不够稳定。为解决上述问题，我们提出STGMFM，一种专为干电极MI-EEG设计的三分支框架，通过双重图结构建模互补的时空依赖关系，并利用多尺度频率混合分支捕捉鲁棒的包络动态，其依据是幅度包络对电极接触变化的敏感性低于瞬时波形。生理学上有意义的连通性先验引导学习，决策层融合生成抗噪共识。在自建的干电极MI-EEG数据集上，STGMFM持续超越对比的CNN/Transformer/图神经网络基线。代码已开源：https://github.com/Tianyi-325/STGMFM。
原文摘要 · Abstract (English)
Dry-electrode Motor Imagery Electroencephalography (MI-EEG) enables fast, comfortable, real-world Brain Computer Interface by eliminating gels and shortening setup for at-home and wearable use.However, dry recordings pose three main issues: lower Signal-to-Noise Ratio with more baseline drift and sudden transients; weaker and noisier data with poor phase alignment across trials; and bigger variances between sessions. These drawbacks lead to larger data distribution shift, making features less stable for MI-EEG tasks.To address these problems, we introduce STGMFM, a tri-branch framework tailored for dry-electrode MI-EEG, which models complementary spatio-temporal dependencies via dual graph orders, and captures robust envelope dynamics with a multi-scale frequency mixing branch, motivated by the observation that amplitude envelopes are less sensitive to contact variability than instantaneous waveforms. Physiologically meaningful connectivity priors guide learning, and decision-level fusion consolidates a noise-tolerant consensus. On our collected dry-electrode MI-EEG, STGMFM consistently surpasses competitive CNN/Transformer/graph baselines. Codes are available at https://github.com/Tianyi-325/STGMFM.
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