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

Issue title: Papers from the 4th International Conference on Biomedical Engineering and Biotechnology (ICBEB2015), 18-21 August 2015, Shanghai, China ``New Paradigms for Biomedical Engineering and Biotechnology''

Article type: Research Article

Authors: Liang, Shuanga | Choi, Kup-Szeb | Qin, Jingc; * | Wang, Qionga | Pang, Wai-Mand | Heng, Pheng-Anne

Affiliations: [a] Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China | [b] School of Nursing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China | [c] Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hone Kong, China | [d] Department of Computer Science, Caritas Institute of Higher Education, Tseung Kwan O, Hong Kong, China | [e] Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China

Correspondence: [*] Corresponding author: Jing Qin, Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hone Kong, China. Tel.: 852 2766 6424 (O); Fax: 852 2364 9663 (O); E-maul: harry.qin@polyu.edu.hk

Keywords: Electroencephalogram (EEG), information flow pattern, multivariate empirical mode decomposition (MEMD), effective connectivity, motor imagery (MI)

DOI: 10.3233/THC-161212

Journal: Technology and Health Care, vol. 24, no. s2, pp. S795-S801, 2016

Published: 13 June 2016
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Abstract

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).

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