Hybrid feature selection for myoelectric signal classification using MICA

Ganesh R. Naik, Dinesh K. Kumar

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
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This paper presents a novel method to enhance the performance of Independent Component Analysis (ICA) of myoelectric signal by decomposing the signal into components originating from different muscles. First, we use Multi run ICA (MICA) algorithm to separate the muscle activities. Pattern classification of the separated signal is performed in the second step with a back propagation neural network. The focus of this work is to establish a simple, yet robust system that can be used to identify subtle complex hand actions and gestures for control of prosthesis and other computer assisted devices. Testing was conducted using several single shot experiments conducted with five subjects. The results indicate that the system is able to classify four different wrist actions with near 100 % accuracy.

Original languageEnglish
Pages (from-to)93-99
Number of pages7
JournalJournal of Electrical Engineering
Issue number2
Publication statusPublished - 2010
Externally publishedYes

Bibliographical note

The non-commercial use of the article will be governed by the Creative Commons Attribution-NonCommercialNoDerivs license


  • Blind source separation (BSS)
  • Human computer interface (HCI)
  • Independent component analysis (ICA)
  • Myoelectric signal (MES)
  • Source separation
  • Surface electromyogram (sEMG)


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