Journal
CLINICAL NEUROPHYSIOLOGY
Volume 115, Issue 6, Pages 1276-1287Publisher
ELSEVIER IRELAND LTD
DOI: 10.1016/j.clinph.2003.12.030
Keywords
electromyography classification; multivariate autoregressive model; principal component analysis
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Objective: We compared the performance of two feature extraction methods for multichannel electromyography (EMG) based arm movement classification. One method was to use a scalar autoregressive model (sAR) for each channel. Another was to model all channels as a whole by a multivariate AR model (mAR). Methods: The classified arm movements included elbow flexion, elbow extension, forearm pronation and internal shoulder rotation. Six-channel bipolar EMG signals were collected from four electrodes fixed on the biceps, triceps, brachioradialis and deltoid. Fifteen two-channel and four three-channel configurations were formed out of these six-channel signals for a comparison of different channel combinations. Leave-one-out cross-validation was adopted for evaluating the classification performance using a parametric statistical classifier. Results: We processed a total of 216 EMG segments obtained from repeated IS performances by three normal subjects. mAR model based feature set achieved a better classification accuracy than sAR did for each configuration. Moreover, significance of improvement was greater than 0.95 for those configurations which consisted of EMG channels that were close spatially. Conclusions: The stronger the cross-correlation among EMG channels the more improvement of classification accuracy one would expect from using a mAR model. (C) 2004 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
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