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Estimation of SSVEP-based EEG complexity using inherent fuzzy entropy


Cao, Z and Prasad, M and Lin, C-T, Estimation of SSVEP-based EEG complexity using inherent fuzzy entropy, Proceedings of the 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 09-12 July 2017, Naples, Italy ISSN 1558-4739 (2017) [Refereed Conference Paper]


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DOI: doi:10.1109/FUZZ-IEEE.2017.8015730


This study considers the dynamic changes of complexity feature by fuzzy entropy measurement and repetitive steady-state visual evoked potential (SSVEP) stimulus. Since brain complexity reflects the ability of the brain to adapt to changing situations, we suppose such adaptation is closely related to the habituation, a form of learning in which an organism decreases or increases to respond to a stimulus after repeated presentations. By a wearable electroencephalograph (EEG) with Fpz and Oz electrodes, EEG signals were collected from 20 healthy participants in one resting and five-times 15 Hz SSVEP sessions. Moreover, EEG complexity feature was extracted by multi-scale Inherent Fuzzy Entropy (IFE) algorithm, and relative complexity (RC) was defined the difference between resting and SSVEP. Our results showed the enhanced frontal and occipital RC was accompanied with increased stimulus times. Compared with the 1st SSVEP session, the RC was significantly higher than the 5th SSVEP session at frontal and occipital areas (p<0.05). It suggested that brain has adapted to changes in stimulus influence, and possibly connected with the habituation. In conclusion, effective evaluation of IFE has a potential EEG signature of complexity in the SSEVP-based experiment.

Item Details

Item Type:Refereed Conference Paper
Keywords:EEG, SSVEP, complexity, 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:132870
Year Published:2017
Deposited By:Information and Communication Technology
Deposited On:2019-05-23
Last Modified:2019-06-17
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