A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees

• Published in: Journal of neuroengineering and rehabilitation Vol. 14; no. 1; p. 2
• Main Authors: Li, Xiangxin, Samuel, Oluwarotimi Williams, Zhang, Xu, Wang, Hui, Fang, Peng, Li, Guanglin
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 07.01.2017; BioMed Central
• Subjects: Algorithms; Amputation; Amputees; Artificial Limbs; Discriminant Analysis; Electrodes; Electroencephalography; Electroencephalography - methods; Electromyography; Electromyography - methods; Humans; Male; Methods; Motion; Movement - physiology; Pattern Recognition, Automated - methods; Physiological aspects; Signal processing; Signal Processing, Computer-Assisted; Wrist Joint; Multifunctional prosthesis; Motion classification; Hybrid interface; Signal Fusion; Amputee; EEG; sEMG; Pattern recognition; Rehabilitation

Most of the modern motorized prostheses are controlled with the surface electromyography (sEMG) recorded on the residual muscles of amputated limbs. However, the residual muscles are usually limited, especially after above-elbow amputations, which would not provide enough sEMG for the control of pro...