Channel selection for simultaneous myoelectric prosthesis control

Abstract

To develop a clinically available prosthesis based on electromyography (EMG) signals, the number of recording electrodes should be as small as possible. In this study, we investigate the possibility of the least absolute shrinkage and selection operator (LASSO) for finding electrode subsets suitable for regression based myoelectric prosthesis control. EMG signals were recorded using 192 electrodes while ten subjects were performing two degree-of-freedom (DoF) wrist movements. Among the whole channels, we selected subsets consisting of 96, 64, 48, 32, 24, 16, 12, and 8 electrodes, respectively, using the LASSO method. As a baseline method, electrode subsets having the same numbers of electrodes were arbitrary selected with regular spacing (uniform selection method). The performance of decoding the movements was estimated using the r-square value. The electrode subsets selected by the LASSO method generally outperformed those chosen by the arbitrary selection method. In particular, the performance of the LASSO method was significantly higher than that of the arbitrary selection method when using the subsets of 8 electrodes. From the analysis results, we could confirm that the LASSO method can be used to select reasonable electrode subsets for regression based myoelectric prosthesis control.