Discrimination of motor imagery tasks via information flow pattern of brain connectivity

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

Research output: Journal article publicationConference articleAcademic researchpeer-review

15 Citations (Scopus)


BACKGROUND: The effective connectivity refers explicitly to the influence that one neural system exerts over another in frequency domain. To investigate the propagation of neuronal activity in certain frequency can help us reveal the mechanisms of information processing by brain. OBJECTIVE: This study investigates the detection of effective connectivity and analyzes the complex brain network connection mode associated with motor imagery (MI) tasks. METHODS: The effective connectivity among the primary motor area is firstly explored using partial directed coherence (PDC) combined with multivariate empirical mode decomposition (MEMD) based on electroencephalography (EEG) data. Then a new approach is proposed to analyze the connection mode of the complex brain network via the information flow pattern. RESULTS: Our results demonstrate that significant effective connectivity exists in the bilateral hemisphere during the tasks, regardless of the left-/right-hand MI tasks. Furthermore, the out-in rate results of the information flow reveal the existence of the contralateral lateralization. The classification performance of left-/right-hand MI tasks can be improved by careful selection of intrinsic mode functions (IMFs). CONCLUSION: The proposed method can provide efficient features for the detection of MI tasks and has great potential to be applied in brain computer interface (BCI).
Original languageEnglish
Pages (from-to)S795-S801
JournalTechnology and Health Care
Publication statusPublished - 13 Jun 2016
Event4th International Conference on Biomedical Engineering and Biotechnology, iCBEB 2015 - Shanghai, China
Duration: 18 Aug 201521 Aug 2015


  • Effective connectivity
  • Electroencephalogram (EEG)
  • Information flow pattern
  • Motor imagery (MI)
  • Multivariate empirical mode decomposition (MEMD)

ASJC Scopus subject areas

  • Biophysics
  • Bioengineering
  • Biomaterials
  • Information Systems
  • Biomedical Engineering
  • Health Informatics


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