Abstract：
: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.
journal_name
Med Biol Eng Computjournal_title
Medical & biological engineering & computingauthors
Huang G,Zhang Z,Zhang D,Zhu Xdoi
10.1007/s11517-012-1024-3subject
Has Abstractpub_date
2013-05-01 00:00:00pages
547-55issue
5eissn
0140-0118issn
1741-0444journal_volume
51pub_type
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