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.

翻译：干电极运动想象脑电图通过消除导电凝胶并缩短设置时间，实现了快速、舒适、适用于现实场景的脑机接口，尤其适合家庭可穿戴应用。然而，干电极记录存在三个主要问题：较低的信噪比伴随更多基线漂移和突发瞬变；信号更弱、噪声更大且试次间相位对齐性差；以及不同会话间更大的数据方差。这些缺陷导致数据分布偏移加剧，使得运动想象脑电任务的特征稳定性下降。为解决这些问题，我们提出了STGMFM——一个专为干电极运动想象脑电设计的三分支框架。该框架通过双图阶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稳健的包络动态特性（其动机在于振幅包络对接触变化的敏感度低于瞬时波形）。生理学意义明确的连接先验指导学习过程，而决策级融合则整合出具有噪声容忍性的共识结果。在我们采集的干电极运动想象脑电数据上，STGMFM持续超越具有竞争力的CNN/Transformer/图神经网络基线模型。代码发布于https://github.com/Tianyi-325/STGMFM。