Effects of repetitive SSVEPs on EEG complexity using multiscale inherent fuzzy entropy

Cao, Z; Ding, W; Wang, YK; Hussain, FK; Al-Jumaily, A; Lin, CT

Effects of repetitive SSVEPs on EEG complexity using multiscale inherent fuzzy entropy

Cao, Z

Ding, W Wang, YK

Hussain, FK

Al-Jumaily, A

Lin, CT

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Publication Type:: Journal Article
Citation:: Neurocomputing, 2019
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Cao, Z https://orcid.org/0000-0003-3656-0328	en_US
dc.contributor.author	Ding, W	en_US
dc.contributor.author	Wang, YK https://orcid.org/0000-0001-8390-2664	en_US
dc.contributor.author	Hussain, FK https://orcid.org/0000-0003-1513-8072	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	Neurocomputing, 2019	en_US
dc.identifier.issn	0925-2312	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134777
dc.description.abstract	© 2019 Elsevier B.V. Multiscale inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity, reflecting the habituation of brain systems. Entropy dynamics are generally believed to reflect the ability of the brain to adapt to a visual stimulus environment. In this study, we explored repetitive steady-state visual evoked potential (SSVEP)-based EEG complexity by assessing multiscale inherent fuzzy entropy with relative measurements. We used a wearable EEG device with Oz and Fpz electrodes to collect EEG signals from 40 participants under the following three conditions: a resting state (closed-eyes (CE) and open-eyes (OE) stimulation with five 15-Hz CE SSVEPs and stimulation with five 20-Hz OE SSVEPs. We noted monotonic enhancement of occipital EEG relative complexity with increasing stimulus times in CE and OE conditions. The occipital EEG relative complexity was significantly higher for the fifth SSVEP than for the first SSEVP (FDR-adjusted p < 0.05). Similarly, the prefrontal EEG relative complexity tended to be significantly higher in the OE condition compared to that in the CE condition (FDR-adjusted p < 0.05). The results also indicate that multiscale inherent fuzzy entropy is superior to other competing multiscale-based entropy methods. In conclusion, EEG relative complexity increases with stimulus times, a finding that reflects the strong habituation of brain systems. These results suggest that multiscale inherent fuzzy entropy is an EEG pattern with which brain complexity can be assessed using repetitive SSVEP stimuli.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP180100656
dc.relation	http://purl.org/au-research/grants/arc/DP180100670
dc.relation.ispartof	Neurocomputing	en_US
dc.relation.isbasedon	10.1016/j.neucom.2018.08.091	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Effects of repetitive SSVEPs on EEG complexity using multiscale inherent fuzzy entropy	en_US
dc.type	Journal Article
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	17 Psychology and Cognitive Sciences	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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2019 Elsevier B.V. Multiscale inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity, reflecting the habituation of brain systems. Entropy dynamics are generally believed to reflect the ability of the brain to adapt to a visual stimulus environment. In this study, we explored repetitive steady-state visual evoked potential (SSVEP)-based EEG complexity by assessing multiscale inherent fuzzy entropy with relative measurements. We used a wearable EEG device with Oz and Fpz electrodes to collect EEG signals from 40 participants under the following three conditions: a resting state (closed-eyes (CE) and open-eyes (OE) stimulation with five 15-Hz CE SSVEPs and stimulation with five 20-Hz OE SSVEPs. We noted monotonic enhancement of occipital EEG relative complexity with increasing stimulus times in CE and OE conditions. The occipital EEG relative complexity was significantly higher for the fifth SSVEP than for the first SSEVP (FDR-adjusted p < 0.05). Similarly, the prefrontal EEG relative complexity tended to be significantly higher in the OE condition compared to that in the CE condition (FDR-adjusted p < 0.05). The results also indicate that multiscale inherent fuzzy entropy is superior to other competing multiscale-based entropy methods. In conclusion, EEG relative complexity increases with stimulus times, a finding that reflects the strong habituation of brain systems. These results suggest that multiscale inherent fuzzy entropy is an EEG pattern with which brain complexity can be assessed using repetitive SSVEP stimuli.

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http://hdl.handle.net/10453/134777