Kim Yeongdae, Stapornchaisit Sorawit, Miyakoshi Makoto, Yoshimura Natsue, Koike Yasuharu
Department of Information and Communications Engineering, Tokyo Institute of Technology, Meguro, Japan.
Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California, San Diego, San Diego, CA, United States.
Front Neurosci. 2020 Dec 1;14:600804. doi: 10.3389/fnins.2020.600804. eCollection 2020.
Surface electromyography (EMG) measurements are affected by various noises such as power source and movement artifacts and adjacent muscle activities. Hardware solutions have been found that use multi-channel EMG signal to attenuate noise signals related to sensor positions. However, studies addressing the overcoming of crosstalk from EMG and the division of overlaid superficial and deep muscles are scarce. In this study, two signal decompositions-independent component analysis and non-negative matrix factorization-were used to create a low-dimensional input signal that divides noise, surface muscles, and deep muscles and utilizes them for movement classification based on direction. In the case of index finger movement, it was confirmed that the proposed decomposition method improved the classification performance with the least input dimensions. These results suggest a new method to analyze more dexterous movements of the hand by separating superficial and deep muscles in the future using multi-channel EMG signals.
表面肌电图（EMG）测量会受到各种噪声的影响，如电源噪声、运动伪迹和相邻肌肉活动。已经发现了一些硬件解决方案，它们使用多通道EMG信号来衰减与传感器位置相关的噪声信号。然而，针对克服EMG串扰以及区分叠加的浅层和深层肌肉的研究却很少。在本研究中，使用了两种信号分解方法——独立成分分析和非负矩阵分解——来创建一个低维输入信号，该信号可将噪声、表层肌肉和深层肌肉区分开来，并将它们用于基于方向的运动分类。在食指运动的情况下，证实了所提出的分解方法在输入维度最少的情况下提高了分类性能。这些结果表明了一种新的方法，未来可通过使用多通道EMG信号分离浅层和深层肌肉来分析手部更灵活的运动。
