Inter-trial analysis of post-movement beta activities in EEG signals using multivariate empirical mode decomposition

Hsiang Chih Chang, Po Lei Lee, Men Tzung Lo, Yu Te Wu, Kuo Wei Wang, Gong Yau Lan

研究成果: 雜誌貢獻文章同行評審

7 引文 斯高帕斯(Scopus)

摘要

Event-related desynchronization/synchronization (ERD/ERS) is a technique to quantify subject's nonphase-locked neural activities underlying specific frequency bands, reactive to external/internal stimulus. However, conventional ERD/ERS studies usually utilize fixed frequency band determined from one or few channels to filter whole-head EEG/MEG data, which may inevitably include task-unrelated signals and result in underestimation of reactive oscillatory activities in multichannel studies. In this study, we adopted multivariate empirical mode decomposition (MEMD) to extract beta-related oscillatory activities in performing self-paced right and left index-finger lifting tasks. The MEMD extracts common modes from all channels in same-index intrinsic mode functions (IMFs) which allows the temporal-frequency features among different channels can be compared in each subband. The beta-band oscillatory activities were further bandpass filtered within trial-specific beta bands determined from sensorimotor-related channels (C3 and C4), and then rectified using amplitude modulation method to detect trial-by-trial beta rebound (BR) values in ERS time courses. The validity of the MEMD approach in BR values extraction has been demonstrated in multichannel EEG study which showed larger BR values than conventional ERS technique. The MEMD-based method enables the trial-by-trial extraction of sensorimotor oscillatory activities which might allow the exploration of subtle brain dynamics in future studies.
原文英語
文章編號6512548
頁(從 - 到)607-615
頁數9
期刊IEEE Transactions on Neural Systems and Rehabilitation Engineering
21
發行號4
DOIs
出版狀態已發佈 - 七月 26 2013
對外發佈Yes

Keywords

  • Electroencephalograph (EEG)
  • event-related synchronization (ERS)
  • multivariate empirical mode decomposition (MEMD)

ASJC Scopus subject areas

  • Neuroscience(all)
  • Biomedical Engineering
  • Computer Science Applications

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