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


CONCLUSIONS
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).
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China.
Shuang
S
Liang
School of Nursing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.
Kup-Sze
KS
Choi
Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hone Kong, China.
Jing
J
Qin
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China.
Qiong
Q
Wang
Department of Computer Science, Caritas Institute of Higher Education, Tseung Kwan O, Hong Kong, China.
Wai-Man
WM
Pang
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China.
Pheng-Ann
PA
Heng
D000465:Algorithms; D004569:Electroencephalography; D006801:Humans; D009044:Motor Cortex / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology*
Electroencephalogram (EEG); effective connectivity; information flow pattern; motor imagery (MI); multivariate empirical mode decomposition (MEMD)