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A best detecting synchrony method in audio STROOP EEG based on wavelet coherence

  • Kang Liu*
  • , Chunying Fang
  • , Haifeng Li
  • , Tingpeng Li
  • *Corresponding author for this work
  • Heilongjiang University of Science and Technology
  • Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Advanced brain function requires different levels of integration and coordination between multi-regional nervous systems, the underlying mechanism is the simultaneous oscillation of various neural networks. EEG is an increasingly method to detect brain function with high temporal resolution and low cost. How to analyze the synchronization phenomenon is the focus of cognitive neuroscience research based on EEG signals. Wavelet coherence is a classical method to evaluate EEG synchronization, but it is uncertain how to use. In this paper, this requires knowledge of the true relationship between signals, hence we compare different measures of functional connectivity on simulated data (unidirectional coupled Hénon maps, and the auditory Stroop EEG), including wavelet cross-spectrum, wavelet correlation, wavelet coherence and FFT coherence. To determine whether synchrony is detected, surrogate data were generated and analyzed, and FFT coherence measures performed best on simulated data. Above all, the parameter optimization method of the wavelet cross-spectrum is proposed with many samples. It is found that the optimized wavelet coherence performed most reliably than FFT coherence.

Original languageEnglish
Title of host publicationCognitive Computing – ICCC 2019 - 3rd International Conference, Held as Part of the Services Conference Federation, SCF 2019, Proceedings
EditorsRuifeng Xu, Jianzong Wang, Liang-Jie Zhang
PublisherSpringer Verlag
Pages197-204
Number of pages8
ISBN (Print)9783030234065
DOIs
StatePublished - 2019
Event3rd International Conference on Cognitive Computing, ICCC 2019, held as part of the Services Conference Federation, SCF 2019 - San Diego, United States
Duration: 25 Jun 201930 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11518 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on Cognitive Computing, ICCC 2019, held as part of the Services Conference Federation, SCF 2019
Country/TerritoryUnited States
CitySan Diego
Period25/06/1930/06/19

Keywords

  • EEG
  • Shannon entropy
  • Synchronous
  • Wavelet coherence

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