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Brain is the most complex organ of human, which serves as the center of controlling most activities. A novel methodology called complex network is capable of characterizing the functional connectivity of human brain by means of graph theoretical measures. We designed and conducted experiments to record electroencephalograph (EEG) signals during left and right hand movement imagery tasks and then probed into brain activities by analyzing multichannel motor imagery signals from the perspective of complex networks. More specifically, we first utilized wavelet time&#x2013;frequency analysis to calculate energy sequence of each channel and then modeled human brain as a graph by treating the channels of scalp EEG as nodes and determining interconnections according to the distance between energy sequences of each channel. The functional connectivity of derived brain networks could be interpreted with characteristics of nodes and edges. Results demonstrated that when subjects imagined left hand movements, the node betweenness centrality (BC) of right sensorimotor area was greater than that of left sensorimotor area. The node BC distribution was roughly opposite when imagining right hand movements. It could be concluded that nodes of contralateral sensorimotor regions were more likely to be activated to control information flows during motor imagery tasks.

A Wavelet Time-Frequency Representation Based Complex Network Method for Characterizing Brain Activities Underlying Motor Imagery Signals