Independent component analysis (ICA) is a general-purpose statistical technique that can linearly transform random data into independent components (ICs) ( Hyvärinen and Oja, 2000). Thereby, additional steps need to be taken in dexterous finger movements estimation using multi-channel EMG signal. Finger movement of high dexterity can be achieved through multiple muscle movements however, this causes significant crosstalk in the forearm EMG measurements. However, they did not dig deep into the dexterous finger movement and nor the structure of muscle synergy per joint movement. Their study investigated deep muscle activities under singular joint movement and confirmed the feasibility of multi-channel EMG signals-based muscles synergy so as to identify deep muscle region. Gazzoni et al., 2014 applied non-negative matrix factorization (NMF) to multi-channel EMG signals and distinguished the position on each forearm per movement of the wrist and single finger joint. However, it is still challenging to detect single muscle activity from EMG ( Schieber, 1995 Keen and Fuglevand, 2004). These noises and crosstalk between muscles can misguide EMG analysis leading to erroneous interpretation hence, there are various studies that focus on attenuating undesirable signals ( De Luca et al., 2010). The surface EMG signal contains different muscle signals and various noises such as baseline noise and movement artifacts ( De Luca et al., 2010). 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.Įlectromyography (EMG) measures the electrical impulses from the muscle contraction induced by the central nervous system for voluntary body movement. In the case of index finger movement, it was confirmed that the proposed decomposition method improved the classification performance with the least input dimensions. 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. However, studies addressing the overcoming of crosstalk from EMG and the division of overlaid superficial and deep muscles are scarce. Hardware solutions have been found that use multi-channel EMG signal to attenuate noise signals related to sensor positions. Surface electromyography (EMG) measurements are affected by various noises such as power source and movement artifacts and adjacent muscle activities. 6Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan.5ATR Brain Information Communication Research Laboratory Group, Kyoto, Japan.4PRESTO, Japan Science and Technology Agency (JST), Tokyo, Japan.3Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan.2Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California, San Diego, San Diego, CA, United States.1Department of Information and Communications Engineering, Tokyo Institute of Technology, Meguro, Japan.Yeongdae Kim 1* Sorawit Stapornchaisit 1 Makoto Miyakoshi 2 Natsue Yoshimura 3,4,5,6 Yasuharu Koike 3*
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