Subtle electromyographic pattern recognition for finger movements: A Pilot study using BSS techniques

Naik, GR; Kumar, DK

Subtle electromyographic pattern recognition for finger movements: A Pilot study using BSS techniques

Naik, GR

Kumar, DK

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Publication Type:: Journal Article
Citation:: Journal of Mechanics in Medicine and Biology, 2012, 12 (4)
Issue Date:: 2012-09-01

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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	2012-09-01	en_US
dc.identifier.citation	Journal of Mechanics in Medicine and Biology, 2012, 12 (4)	en_US
dc.identifier.issn	0219-5194	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26334
dc.description.abstract	In the recent past, blind source separation (BSS) algorithms using multivariate statistical data analysis technique have been successfully used for source identification and separation in the field of biomedical and statistical signal processing. Recently numbers of different BSS techniques have been developed. With BSS methods being the feasible method for source separation and decomposition of biosignals, it is important to compare the different techniques and determine the most suitable method for the applications. This paper presents the performance of five BSS algorithms (SOBI, TDSEP, FastICA, JADE and Infomax) for decomposition of sEMG to identify subtle finger movements. It is observed that BSS algorithms based on second-order statistics (SOBI and TDSEP) gives better performance compared to algorithms based on higher-order statistics (FastICA, JADE and infomax). © 2012 World Scientific Publishing Company.	en_US
dc.relation.ispartof	Journal of Mechanics in Medicine and Biology	en_US
dc.relation.isbasedon	10.1142/S0219519412005009	en_US
dc.title	Subtle electromyographic pattern recognition for finger movements: A Pilot study using BSS techniques	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	12	en_US
utslib.for	0999 Other Engineering	en_US
utslib.for	0903 Biomedical Engineering	en_US
utslib.for	0912 Materials Engineering	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	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	12	en_US

Abstract:

In the recent past, blind source separation (BSS) algorithms using multivariate statistical data analysis technique have been successfully used for source identification and separation in the field of biomedical and statistical signal processing. Recently numbers of different BSS techniques have been developed. With BSS methods being the feasible method for source separation and decomposition of biosignals, it is important to compare the different techniques and determine the most suitable method for the applications. This paper presents the performance of five BSS algorithms (SOBI, TDSEP, FastICA, JADE and Infomax) for decomposition of sEMG to identify subtle finger movements. It is observed that BSS algorithms based on second-order statistics (SOBI and TDSEP) gives better performance compared to algorithms based on higher-order statistics (FastICA, JADE and infomax). © 2012 World Scientific Publishing Company.

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http://hdl.handle.net/10453/26334