Journal
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
Volume 51, Issue 5, Pages 547-555Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s11517-012-1024-3
Keywords
EMG; CSSP; Spatio-spectral filter
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Funding
- National Basic Research Program (973 Program) of China [2011CB013305]
- Science and Technology Commission of Shanghai Municipality [11JC1406000]
- State Key Laboratory of Mechanical System and Vibration [MSVZD201204]
- University of Hong Kong Seed Funding Programme for Basic Research [201203159009]
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In this paper, we proposed to utilize a novel spatio-spectral filter, common spatio-spectral pattern (CSSP), to improve the classification accuracy in identifying intended motions based on low-density surface electromyography (EMG). Five able-bodied subjects and a transradial amputee participated in an experiment of eight-task wrist and hand motion recognition. Low-density (six channels) surface EMG signals were collected on forearms. Since surface EMG signals are contaminated by large amount of noises from various sources, the performance of the conventional time-domain feature extraction method is limited. The CSSP method is a classification-oriented optimal spatio-spectral filter, which is capable of separating discriminative information from noise and, thus, leads to better classification accuracy. The substantially improved classification accuracy of the CSSP method over the time-domain and other methods is observed in all five able-bodied subjects and verified via the cross-validation. The CSSP method can also achieve better classification accuracy in the amputee, which shows its potential use for functional prosthetic control.
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