NDLI: Whitening of the electromyogram for improved classification accuracy in prosthesis control

Content Provider	IEEE Xplore Digital Library
Author	Lukai Liu Pu Liu Clancy, E.A. Scheme, E. Englehart, K.B.
Copyright Year	2012
Description	Author affiliation: Inst. of Biomed. Eng., Univ. of New Brunswick, Fredericton, NB, Canada (Scheme, E.; Englehart, K.B.) \|\| Electr. & Comput. Eng. Dept., Worcester Polytech. Inst., Worcester, MA, USA (Lukai Liu; Pu Liu; Clancy, E.A.)
Abstract	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).
Sponsorship	IEEE Eng. Medicine Biol. Soc.
Starting Page	2627
Ending Page	2630
File Size	921928
Page Count	4
File Format	PDF
ISBN	9781424441198
ISSN	1557170X
e-ISBN	9781457717871
DOI	10.1109/EMBC.2012.6346503
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electromyography Electrodes Accuracy Power harmonic filters Maximum likelihood detection Nonlinear filters Pattern recognition
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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