Mental performance classification using fused multilevel feature generation with EEG signals

Aydemir, E; Baygin, M; Dogan, S; Tuncer, T; Barua, PD; Chakraborty, S; Faust, O; Arunkumar, N; Kaysi, F; Acharya, UR

Mental performance classification using fused multilevel feature generation with EEG signals

Aydemir, E Baygin, M Dogan, S Tuncer, T Barua, PD Chakraborty, S

Faust, O Arunkumar, N Kaysi, F Acharya, UR

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Publisher:: Taylor & Francis
Publication Type:: Journal Article
Citation:: International Journal of Healthcare Management, 2022, pp. 1-14
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Aydemir, E
dc.contributor.author	Baygin, M
dc.contributor.author	Dogan, S
dc.contributor.author	Tuncer, T
dc.contributor.author	Barua, PD
dc.contributor.author	Chakraborty, S https://orcid.org/0000-0002-0102-5424
dc.contributor.author	Faust, O
dc.contributor.author	Arunkumar, N
dc.contributor.author	Kaysi, F
dc.contributor.author	Acharya, UR
dc.date.accessioned	2023-04-04T04:04:04Z
dc.date.available	2023-04-04T04:04:04Z
dc.date.issued	2022-01-01
dc.identifier.citation	International Journal of Healthcare Management, 2022, pp. 1-14
dc.identifier.issn	2047-9700
dc.identifier.issn	2047-9719
dc.identifier.uri	http://hdl.handle.net/10453/169104
dc.description.abstract	Mental performance classification is a critical issue for brain-computer interfaces. Accurate and reliable classification of good or bad mental performance gives important clues for the preliminary diagnosis of some diseases and mental stress. In this work, we put forward an objective artificial intelligence model to quantify the clarity of thought during mental arithmetic tasks. The proposed model consists of: (i) multilevel feature extraction based on statistical and texture analysis methods, (ii) feature ranking and selection with a Chi2 method, (iii) classification, and (iv) weightless majority voting classifier. The novelty of the presented model comes from multilevel fused feature generation. The presented model was developed using 20 channel electroencephalography data from 36 subjects. The signals were captured while the subjects were performing mental arithmetic tasks. The individual datasets were labeled as either good or bad, based on the task results. We have obtained an accuracy of 96.77% using O2 channel with a k-nearest neighbor classifier and reached 100.0% accuracy with the majority voting classifier. Our results indicate that it is possible to determine mental performance with artificial intelligence. That might be a steppingstone to establish objective measures for the clarity of thought during a wide range of mental tasks.
dc.language	en
dc.publisher	Taylor & Francis
dc.relation.ispartof	International Journal of Healthcare Management
dc.relation.isbasedon	10.1080/20479700.2022.2130645
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	1117 Public Health and Health Services, 1503 Business and Management, 1505 Marketing
dc.title	Mental performance classification using fused multilevel feature generation with EEG signals
dc.type	Journal Article
utslib.for	1117 Public Health and Health Services
utslib.for	1503 Business and Management
utslib.for	1505 Marketing
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 Civil and Environmental Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-04T04:04:02Z
pubs.publication-status	Published

Abstract:

Mental performance classification is a critical issue for brain-computer interfaces. Accurate and reliable classification of good or bad mental performance gives important clues for the preliminary diagnosis of some diseases and mental stress. In this work, we put forward an objective artificial intelligence model to quantify the clarity of thought during mental arithmetic tasks. The proposed model consists of: (i) multilevel feature extraction based on statistical and texture analysis methods, (ii) feature ranking and selection with a Chi2 method, (iii) classification, and (iv) weightless majority voting classifier. The novelty of the presented model comes from multilevel fused feature generation. The presented model was developed using 20 channel electroencephalography data from 36 subjects. The signals were captured while the subjects were performing mental arithmetic tasks. The individual datasets were labeled as either good or bad, based on the task results. We have obtained an accuracy of 96.77% using O2 channel with a k-nearest neighbor classifier and reached 100.0% accuracy with the majority voting classifier. Our results indicate that it is possible to determine mental performance with artificial intelligence. That might be a steppingstone to establish objective measures for the clarity of thought during a wide range of mental tasks.

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http://hdl.handle.net/10453/169104