A hybrid multi-channel surface EMG decomposition approach by combining CKC and FCM

A hybrid approach is successfully developed in this study by combining the fuzzy C means (FCM) clustering method and Convolution Kernel Compensation (CKC) method for multi-channel surface electromyogram (EMG) decomposition. The FCM is utilized to estimate the initial innervation pulse trains (IPTs)...

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Bibliographic Details
Published in2013 6th International IEEE/EMBS Conference on Neural Engineering (NER) pp. 335 - 338
Main Authors Yong Ning, Shanan Zhu, Xiangjun Zhu, Yingchun Zhang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2013
Subjects
Convolution
Educational institutions
Electromyography
Kernel
Signal to noise ratio
Surface reconstruction
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Summary:A hybrid approach is successfully developed in this study by combining the fuzzy C means (FCM) clustering method and Convolution Kernel Compensation (CKC) method for multi-channel surface electromyogram (EMG) decomposition. The FCM is utilized to estimate the initial innervation pulse trains (IPTs) of motor units (MUs) from a few channel surface EMG signals, the CKC method is then employed to estimate the final IPTs. Computer simulation results demonstrate the improved efficiency and accuracy of the hybrid approach compared to the classic CKC method.
ISSN:1948-3546
1948-3554
DOI:10.1109/NER.2013.6695940