A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees
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- 作者：Xiangxin Li ; Oluwarotimi Williams Samuel…
- 关键词：Amputee ; EEG ; Hybrid interface ; Motion classification ; Multifunctional prosthesis ; Pattern recognition ; Rehabilitation ; sEMG ; Signal Fusion
- 刊名：Journal of NeuroEngineering and Rehabilitation
- 出版年：2017
- 出版时间：December 2017
- 年：2017
- 卷：14
- 期：1
- 全文大小：4372KB
- 刊物主题：Neurosciences; Neurology; Rehabilitation Medicine; Biomedical Engineering;
- 出版者：BioMed Central
- ISSN：1743-0003
- 卷排序：14
文摘
BackgroundMost 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 prostheses with multiple degrees of freedom. Signal fusion is a possible approach to solve the problem of insufficient control commands, where some non-EMG signals are combined with sEMG signals to provide sufficient information for motion intension decoding. In this study, a motion-classification method that combines sEMG and electroencephalography (EEG) signals were proposed and investigated, in order to improve the control performance of upper-limb prostheses.