EEG Signal Discrimination with Permutation Entropy

Youpeng Yang, Haolan Zhang, Sanghyuk Lee*

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

The information analysis of the electroencephalogram (EEG) signal is carried out by granulation and reciprocal entropy (PeEn). The analysis of the EEG signal is obtained by experimental activity. Due to its complexity and multichannel characteristic, together with granular computing (GrC) and PeEn are used to analyze the EEG signal. The EEG signal consists of 32 channels of data and the experimental data are used to discriminate patterns, with experimental focus on considering real and thinking actions. The time-series EEG signals were granularized according to the changes in the signal and analyzed by PeEn coding and Fuzzy C-Means (FCM) algorithm. Because there are two main actions, i.e., left-handed, and right-handed actions were clearly delineated. In addition, we provide the GrC algorithm to prove the boundary problem with the help of Hilbert-Huang transform. The obtained results show an advanced approach for analyzing EEG signals, which can be the basis for solving complex multichannel data analysis.

Original languageEnglish
Title of host publicationBrain Informatics - 14th International Conference, BI 2021, Proceedings
EditorsMufti Mahmud, M Shamim Kaiser, Stefano Vassanelli, Qionghai Dai, Ning Zhong
PublisherSpringer Science and Business Media Deutschland GmbH
Pages519-528
Number of pages10
ISBN (Print)9783030869922
DOIs
Publication statusPublished - Sept 2021
Event14th International Conference on Brain Informatics, BI 2021 - Virtual, Online
Duration: 17 Sept 202119 Sept 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12960 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Brain Informatics, BI 2021
CityVirtual, Online
Period17/09/2119/09/21

Keywords

  • Data uncertainty
  • EEG signal
  • Granular computing
  • Granulation
  • Permutation entropy

Cite this