融合脑电与肌电信号，提升运动意图识别准确率
EEG-EMG FAConformer: Frequency Aware Conv-Transformer for the fusion of EEG and EMG
- 设计频带注意力模块，精准捕捉脑电信号频率特征
- 在Jeong2020数据集上超越现有方法，表现稳定可靠
- 适合康复训练与脑机接口领域研究人员参考
运动模式识别是脑机接口用于运动功能康复的主要范式，也是最易推广的应用。近年来，研究者建议在基于运动想象的脑机接口康复训练系统中，鼓励患者同时执行真实运动。肌电（EMG）信号是评估运动执行最直接的生理信号。多模态信号融合对解码运动模式具有实际意义。为此，本文提出一种针对脑电（EEG）与肌电（EMG）信号的多模态运动模式识别算法：EEG-EMG FAConformer，该方法包含多个与时序和频率信息相关的注意力模块，用于运动模式识别。特别地，设计了频带注意力模块以高效准确地编码脑电信息。此外，还提出了多尺度融合模块、独立通道特定卷积模块（ICSCM）以及融合模块，可有效消除EEG与EMG信号中的无关信息，并充分挖掘隐藏动态特性。大量实验表明，该方法在Jeong2020数据集上优于现有方法，展现出优异性能、高鲁棒性及显著稳定性。
原文摘要 · Abstract (English)
Motor pattern recognition paradigms are the main forms of Brain-Computer Interfaces(BCI) aimed at motor function rehabilitation and are the most easily promoted applications. In recent years, many researchers have suggested encouraging patients to perform real motor control execution simultaneously in MI-based BCI rehabilitation training systems. Electromyography (EMG) signals are the most direct physiological signals that can assess the execution of movements. Multimodal signal fusion is practically significant for decoding motor patterns. Therefore, we introduce a multimodal motion pattern recognition algorithm for EEG and EMG signals: EEG-EMG FAConformer, a method with several attention modules correlated with temporal and frequency information for motor pattern recognition. We especially devise a frequency band attention module to encode EEG information accurately and efficiently. What's more, modules like Multi-Scale Fusion Module, Independent Channel-Specific Convolution Module(ICSCM), and Fuse Module which can effectively eliminate irrelevant information in EEG and EMG signals and fully exploit hidden dynamics are developed and show great effects. Extensive experiments show that EEG-EMG FAConformer surpasses existing methods on Jeong2020 dataset, showcasing outstanding performance, high robustness and impressive stability.
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