Source identification and separation using sub-band ICA of sEMG

Naik, GR; Kumar, DK; Palaniswami, M

Source identification and separation using sub-band ICA of sEMG

Naik, GR

Kumar, DK Palaniswami, M

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Publication Type:: Conference Proceeding
Citation:: IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2008
Issue Date:: 2008-12-01

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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.contributor.author	Palaniswami, M	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2008	en_US
dc.identifier.isbn	1424424089	en_US
dc.identifier.isbn	9781424424085	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32006
dc.description.abstract	Source identification and separation of number of active muscles during a complex action is useful information to identify the action, and to determine pathologies. Biosignals such as surface electromyogram are a result of the summation of electrical activity of a number of sources. The complexity of the anatomy and actions results difficulty in identifying the number of active sources from the multiple channel recordings. ICA has been applied to sEMG to separate the signals originating from different sources. But it is often difficult to determine the number of active sources that may vary between different actions and gestures. This paper reports research conducted to evaluate the use of sub-band ICA for the separation of bioelectric signals when the number of active sources may not be known. The paper proposes the use of value of the determinant of the global matrix generated using sub-band ICA for identifying the number of active sources. The results indicate that the technique is successful in identifying the number of active muscles for complex hand gestures.	en_US
dc.relation.ispartof	IEEE Region 10 Annual International Conference, Proceedings/TENCON	en_US
dc.relation.isbasedon	10.1109/TENCON.2008.4766726	en_US
dc.title	Source identification and separation using sub-band ICA of sEMG	en_US
dc.type	Conference Proceeding
utslib.for	090609 Signal Processing	en_US
utslib.for	090302 Biomechanical Engineering	en_US
utslib.for	090399 Biomedical Engineering not elsewhere classified	en_US
dc.location.activity	Hyderabad
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.publication-status	Published	en_US

Abstract:

Source identification and separation of number of active muscles during a complex action is useful information to identify the action, and to determine pathologies. Biosignals such as surface electromyogram are a result of the summation of electrical activity of a number of sources. The complexity of the anatomy and actions results difficulty in identifying the number of active sources from the multiple channel recordings. ICA has been applied to sEMG to separate the signals originating from different sources. But it is often difficult to determine the number of active sources that may vary between different actions and gestures. This paper reports research conducted to evaluate the use of sub-band ICA for the separation of bioelectric signals when the number of active sources may not be known. The paper proposes the use of value of the determinant of the global matrix generated using sub-band ICA for identifying the number of active sources. The results indicate that the technique is successful in identifying the number of active muscles for complex hand gestures.

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http://hdl.handle.net/10453/32006