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


Cao, Z and Ding, W and Wang, Y-K and Hussain, FK and Al-Jumaily, A and Lin, C-T, Effects of repetitive SSVEPs on EEG complexity using multiscale inherent fuzzy entropy, Neurocomputing, 389 pp. 198-206. ISSN 0925-2312 (2020) [Refereed Article]

Copyright Statement

2019 Elsevier B.V. All rights reserved.

DOI: doi:10.1016/j.neucom.2018.08.091


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.

Item Details

Item Type:Refereed Article
Keywords:EEG, entropy, SSVEP, complexity, multiscale inherent fuzzy entropy
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence, surveillance and space
UTAS Author:Cao, Z (Dr Zehong Cao)
ID Code:132867
Year Published:2020 (online first 2019)
Web of Science® Times Cited:60
Deposited By:Information and Communication Technology
Deposited On:2019-05-23
Last Modified:2020-07-24

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