Addressing source separation and identification issues in surface EMG using blind source separation.

Naik, GR; Kumar, DK; Palaniswami, M

Addressing source separation and identification issues in surface EMG using blind source separation.

Naik, GR

Kumar, DK Palaniswami, M

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Publication Type:: Conference Proceeding
Citation:: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2008, pp. 1124 - 1127
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	Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2008, pp. 1124 - 1127	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30649
dc.description.abstract	Source separation and identification is one of the challenging areas in the bio signal processing. The processing of Electromyographic (EMG) signals can be viewed as the identification and separation of a series of overlapping sources of muscle activity with slowly varying source distribution and/or levels of activity. Blind source separation (BSS) techniques such as independent component analysis (ICA) lend themselves well to the analysis of such problems. The problem, however, still remains largely ill-posed even through the use of powerful assumptions such as those posed in ICA and other such techniques. It is generally the case in EMG signals that a certain level of a priori knowledge is available on the spatio-temporal and/or frequency distribution of the activities of interest, based on neurophysiological expectations. Here we describe limitations and applications of BSS on surface EMG. The problems we consider include the analysis of facial sEMG recordings during vowel utterance and analysis of hand EMG during finger and wrist movements.	en_US
dc.relation.ispartof	Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference	en_US
dc.subject.mesh	Muscle, Skeletal	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Electromyography	en_US
dc.subject.mesh	Sensitivity and Specificity	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Action Potentials	en_US
dc.subject.mesh	Muscle Contraction	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Artificial Intelligence	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.title	Addressing source separation and identification issues in surface EMG using blind source separation.	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	Vancouver, BC
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 separation and identification is one of the challenging areas in the bio signal processing. The processing of Electromyographic (EMG) signals can be viewed as the identification and separation of a series of overlapping sources of muscle activity with slowly varying source distribution and/or levels of activity. Blind source separation (BSS) techniques such as independent component analysis (ICA) lend themselves well to the analysis of such problems. The problem, however, still remains largely ill-posed even through the use of powerful assumptions such as those posed in ICA and other such techniques. It is generally the case in EMG signals that a certain level of a priori knowledge is available on the spatio-temporal and/or frequency distribution of the activities of interest, based on neurophysiological expectations. Here we describe limitations and applications of BSS on surface EMG. The problems we consider include the analysis of facial sEMG recordings during vowel utterance and analysis of hand EMG during finger and wrist movements.

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http://hdl.handle.net/10453/30649