Title :
Can transcranial direct current stimulation enhance performance of myoelectric control for multifunctional prosthesis?
Author :
Lizhi Pan ; Dingguo Zhang ; Renquan Duan ; Xiangyang Zhu
Author_Institution :
Sch. of Mech. Eng., Shanghai Jiao Tong Univ., Shanghai, China
Abstract :
Pattern recognition based myoelectric control has been studied by many researchers. However, the classification accuracy was pretty low for amputees towards multifunctional prosthesis control in practice. In this work, a novel method of transcranial direct current stimulation (tDCS) which can modulate brain activity was used to enhance performance for myoelectric prosthesis control. The pilot study was conducted on three able-bodied subjects and one transradial amputee. Surface electromyography (EMG) signals were acquired from both arms when performing eleven hand and wrist motions in pre-tDCS and post-tDCS sessions. Time domain (TD) features and linear discriminant analysis (LDA) classifier were adopted to process EMG. For the non-dominant hand of the healthy subjects, active anodal tDCS of the contralateral primary motor cortex was able to significantly improve average classification accuracy by 3.82% (p <; 0.05), while sham tDCS could not have such effect (p > 0.05). For amputated (phantom) hand of the amputee, active anodal tDCS was able to significantly improve average classification accuracy by 12.56%, while sham tDCS could not have such effect. For the dominant hand and intact hand, the average classification accuracies were stable and not significantly improved using either active tDCS or sham tDCS. The results show that tDCS is a powerful noninvasive method to modulate brain function and enhance EMG classification performance especially for the amputated hand towards multifunctional prosthesis control. The method proposed has a huge potential to promote EMG pattern recognition based control scheme to clinical application.
Keywords :
anodes; biomechanics; biomedical electrodes; brain; data acquisition; electromyography; feature extraction; medical control systems; medical signal processing; neurophysiology; patient treatment; phantoms; prosthetics; signal classification; time-domain analysis; EMG classification performance; EMG pattern recognition based control; EMG signal acquisition; LDA classifier; active anodal tDCS effect; amputated hand; average classification accuracy; brain activity modulation; brain function modulation; clinical application; contralateral primary motor cortex; hand motions; intact hand; linear discriminant analysis; multifunctional prosthesis control; myoelectric control performance enhancement; myoelectric prosthesis control; nondominant hand; noninvasive method; phantom hand; post-tDCS sessions; pre-tDCS sessions; sham tDCS effect; surface electromyography; time domain features; transcranial direct current stimulation; transradial amputee; wrist motions; Accuracy; DC motors; Electrodes; Electromyography; Pattern recognition; Prosthetics; Wrist;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE
Conference_Location :
Chicago, IL
DOI :
10.1109/EMBC.2014.6944393