Construction and analysis of cortical–muscular functional network based on EEG-EMG coherence using wavelet coherence

Abstract Research on the brain functional network is important in understanding the normal function of the brain and diagnosing neuropsychiatric diseases. Inspired by the brain functional network, we constructed a cortical–muscular functional network using electroencephalography and electromyography to explore the motion control mechanism of the central nervous system and understand the organization and coordination mechanisms of limb motion control. In the process of constructing the network, 12 signal acquisition channels were selected as nodes, and the wavelet coherence is used as the index of connection between network nodes. Based on the original network, we used a fixed weighted edge and threshold methods to remove weak weighted edges and compare the performance of the two methods. The experimental results showed that the constructed network had a higher clustering coefficient, and the smaller characteristic path length indicated a small-world characteristic. At the same time, the weighted characteristic path length and weighted clustering coefficient of the functional network simplified by the threshold method can show promising classification accuracy under Fisher and artificial neural network.