Estimation of SSVEP-based EEG Complexity using Inherent Fuzzy Entropy

Zehong Cao, Mukesh Prasad, Chin-Teng Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

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.
Original languageEnglish
Title of host publication2017 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
PublisherIEEE
ISBN (Print)978-1-5090-6034-4
StatePublished - 9 Jul 2017

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Keywords

  • EEG
  • SSVEP
  • Complexity
  • Inherent Fuzzy Entropy

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    Cao, Z., Prasad, M., & Lin, C-T. (2017). Estimation of SSVEP-based EEG Complexity using Inherent Fuzzy Entropy. In 2017 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) (IEEE International Conference on Fuzzy Systems). IEEE.