Classification of motor imagery tasks using phase synchronization analysis of EEG based on multivariate empirical mode decomposition

Shuang Liang, Kup Sze Choi, Jing Qin, Wai Man Pang, Pheng Ann Heng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Phase synchronization has been employed to study brain networks and connectivity patterns. The phase locking value (PLV) is one of the most effective measures widely used for phase synchronization analysis. We first calculate the PLVs of the pair-wise intrinsic mode functions (IMFs) based on multivariate empirical mode decomposition (MEMD) method. Next, the average PLV of the prominent pairs relative to the rest duration is adopted for the classification of motor imagery (MI) tasks. Comparative analysis with the EMD-based PLV method, the proposed method has a significant increase in feature separability for most subjects. This paper demonstrates that MEMD-based PLV method can provide an effective feature in the MI task classification and the potential for BCI applications.
Original languageEnglish
Title of host publicationICIST 2014 - Proceedings of 2014 4th IEEE International Conference on Information Science and Technology
PublisherIEEE
Pages674-677
Number of pages4
ISBN (Electronic)9781479948086
DOIs
Publication statusPublished - 1 Jan 2014
Event2014 4th IEEE International Conference on Information Science and Technology, ICIST 2014 - Shenzhen, China
Duration: 26 Apr 201428 Apr 2014

Conference

Conference2014 4th IEEE International Conference on Information Science and Technology, ICIST 2014
Country/TerritoryChina
CityShenzhen
Period26/04/1428/04/14

Keywords

  • brain connectivity
  • Electroencephalogram (EEG)
  • motor imagery (MI)
  • multivariate empirical mode decomposition (MEMD)
  • phase synchronization

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

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