Combining ICA Clustering and Power Spectral Density for Feature Extraction of Mental Fatigue of Spinal Cord Injury Patients

Chai, R; Tran, Y; Ling, SH; Craig, A; Nguyen, HT

Combining ICA Clustering and Power Spectral Density for Feature Extraction of Mental Fatigue of Spinal Cord Injury Patients

Chai, R

Tran, Y Ling, SH

Craig, A Nguyen, HT

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2019, pp. 530 - 533
Issue Date:: 2019-07-01

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Field	Value	Language
dc.contributor.author	Chai, R https://orcid.org/0000-0002-1922-7024	en_US
dc.contributor.author	Tran, Y	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Craig, A	en_US
dc.contributor.author	Nguyen, HT	en_US
dc.date.issued	2019-07-01	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2019, pp. 530 - 533	en_US
dc.identifier.isbn	9781538613115	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/136711
dc.description.abstract	© 2019 IEEE. This paper presents the combination of clustering-based independent component analysis (ICASSO) and power spectral density (PSD) as a features extractor of mental fatigue from spinal cord injury (SCI) patients. Initially, the results show that SCI and abled-bodied groups have no differences in EEG for alert and mental fatigue states. Further, the coefficient determination (R2) is calculated for testing the variation of data alert vs. fatigue on the SCI group, resulting in a lower R2 for proposed combination of ICASSO and PSD method compared to the PSD method only. With the lower R2 values, this shows that the proposed method ICASSO and PSD is able to provide superior distinction for separating fatigue vs. alert data variation. The statistical significance is found across four EEG bands and EEG channels.	en_US
dc.relation.ispartof	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS	en_US
dc.relation.isbasedon	10.1109/EMBC.2019.8857137	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Spinal Cord Injuries	en_US
dc.subject.mesh	Electroencephalography	en_US
dc.subject.mesh	Cluster Analysis	en_US
dc.subject.mesh	Mental Fatigue	en_US
dc.subject.mesh	Algorithms	en_US
dc.title	Combining ICA Clustering and Power Spectral Density for Feature Extraction of Mental Fatigue of Spinal Cord Injury Patients	en_US
dc.type	Conference Proceeding
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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2021-10-31T00:00:00+1100
pubs.publication-status	Published	en_US

Abstract:

© 2019 IEEE. This paper presents the combination of clustering-based independent component analysis (ICASSO) and power spectral density (PSD) as a features extractor of mental fatigue from spinal cord injury (SCI) patients. Initially, the results show that SCI and abled-bodied groups have no differences in EEG for alert and mental fatigue states. Further, the coefficient determination (R2) is calculated for testing the variation of data alert vs. fatigue on the SCI group, resulting in a lower R2 for proposed combination of ICASSO and PSD method compared to the PSD method only. With the lower R2 values, this shows that the proposed method ICASSO and PSD is able to provide superior distinction for separating fatigue vs. alert data variation. The statistical significance is found across four EEG bands and EEG channels.

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http://hdl.handle.net/10453/136711