title: Spatio-spectral filters for low-density surface electromyographic signal classification.
abstract: In this paper, we proposed to utilize a novel spatio-spectral filter, common spatio-spectral pattern (CSSP), to improve the classification accuracy in identifying intended motions based on low-density surface electromyography (EMG). Five able-bodied subjects and a transradial amputee participated in an experiment of eight-task wrist and hand motion recognition. Low-density (six channels) surface EMG signals were collected on forearms. Since surface EMG signals are contaminated by large amount of noises from various sources, the performance of the conventional time-domain feature extraction method is limited. The CSSP method is a classification-oriented optimal spatio-spectral filter, which is capable of separating discriminative information from noise and, thus, leads to better classification accuracy. The substantially improved classification accuracy of the CSSP method over the time-domain and other methods is observed in all five able-bodied subjects and verified via the cross-validation. The CSSP method can also achieve better classification accuracy in the amputee, which shows its potential use for functional prosthetic control.
authors: State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University, Shanghai 200240, China. huanggan1982@gmail.com
authors: Gan
authors: G
authors: Huang
authors: Zhiguo
authors: Z
authors: Zhang
authors: Dingguo
authors: D
authors: Zhang
authors: Xiangyang
authors: X
authors: Zhu
mesh_terms: D000465:Algorithms; D000674:Amputees / Q000534:rehabilitation; D004576:Electromyography / Q000379:methods*; D005542:Forearm / Q000502:physiology; D006225:Hand / Q000502:physiology; D006801:Humans; D009068:Movement / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D014955:Wrist Joint / Q000502:physiology
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