Hybrid feature selection for myoelectric signal classification using MICA

Naik, GR; Kumar, DK

Hybrid feature selection for myoelectric signal classification using MICA

Naik, GR

Kumar, DK

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Publication Type:: Journal Article
Citation:: Journal of Electrical Engineering, 2010, 61 (2), pp. 93 - 99
Issue Date:: 2010-01-01

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Field	Value	Language
dc.contributor.author	Naik, GR https://orcid.org/0000-0003-1790-9838	en_US
dc.contributor.author	Kumar, DK	en_US
dc.date.issued	2010-01-01	en_US
dc.identifier.citation	Journal of Electrical Engineering, 2010, 61 (2), pp. 93 - 99	en_US
dc.identifier.issn	1335-3632	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26647
dc.description.abstract	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. © 2010 FEI STU.	en_US
dc.relation.ispartof	Journal of Electrical Engineering	en_US
dc.relation.isbasedon	10.2478/v10187-010-0013-8	en_US
dc.subject.classification	Electrical & Electronic Engineering	en_US
dc.title	Hybrid feature selection for myoelectric signal classification using MICA	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	61	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	090399 Biomedical Engineering not elsewhere classified	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	61	en_US

Abstract:

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. © 2010 FEI STU.

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http://hdl.handle.net/10453/26647