Whitening of the electromyogram for improved classification accuracy in prosthesis control.
The electromyogram (EMG) signal has been used as the command input to myoelectric prostheses. A common control scheme is based on classifying the EMG signals from multiple electrodes into one of several distinct classes of user intent/function. In this work, we investigated the use of EMG whitening as a preprocessing step to EMG pattern recognition. Whitening is known to decorrelate the EMG signal, with improved performance shown in the related applications of EMG amplitude estimation and EMG-torque processing. We reanalyzed the EMG signals recorded from 10 electrodes placed circumferentially around the forearm of 10 intact subjects and 5 amputees. The coefficient of variation of two time-domain features--mean absolute value and signal length--was significantly reduced after whitening. Pre-whitened classification models using these features, along with autoregressive power spectrum coefficients, added approximately five percentage points to their classification accuracy. Improvement was best using smaller window durations (<100 ms).
Electrical and Computer Engineering Department, Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA 01609, USA. lliu35@wpi.edu
Lukai
L
Liu
Pu
P
Liu
Edward A
EA
Clancy
Erik
E
Scheme
Kevin B
KB
Englehart
D000465:Algorithms; D000674:Amputees; D004566:Electrodes; D004576:Electromyography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated