Common spatial-spectral analysis of EMG signals for multiday and multiuser myoelectric interface

Abstract

Practical implementation of myoelectric interfaces have been largely hindered by cumbersome training and retraining procedures required for use across multiple days or for multiple users. We thus present a common spatial-spectral analysis (CSSA) framework to eliminate the need for retraining over multiple days or for multiple users. The CSSA is implemented through spectral decomposition and common modes analysis, maximumly utilizing the common spatial-spectral electromyography (EMG) mode from multiple days or for multiple users. Experiments involving two scenarios were conducted to simulate the application of multiday or multiuser myoelectric interface. Eight healthy and three amputee subjects participated in the first experiment for ten consecutive days, and seven healthy subjects participated in the second experiment involving a multiuser interface. Experimental results demonstrated that the control performance without retraining the myoelectric interface with the CSSA was significantly improved, and the classifier model pre-trained by background data under CSSA enabled EMG signals from new days or users to be recognized without training or retraining. The results could serve as a foundation for practical implementation of myoelectric interfaces.