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In the decoding process, the classifier is one of the key factors, and the graph information of the EEG was ignored by most researchers. In this paper, a graph convolutional network (GCN) based on functional connectivity was proposed to decode the motor intention of four fine parts movements (shoulder, elbow, wrist, hand). First, event-related desynchronization was analyzed to reveal the differences between the four classes. Second, functional connectivity was constructed by using synchronization likelihood (SL), phase-locking value (PLV), H index (H), mutual information (MI), and weighted phase-lag index (WPLI) to acquire the electrode pairs with a difference. Subsequently, a GCN and convolutional neural networks (CNN) were performed based on functional topological structures and time points, respectively. The results demonstrated that the proposed method achieved a decoding accuracy of up to 92.81% in the four-class task. Besides, the combination of GCN and functional connectivity can promote the development of BCI. <\/jats:p>","DOI":"10.1142\/s0129065721500477","type":"journal-article","created":{"date-parts":[[2021,10,24]],"date-time":"2021-10-24T10:55:18Z","timestamp":1635072918000},"source":"Crossref","is-referenced-by-count":30,"title":["Motor Intention Decoding from the Upper Limb by Graph Convolutional Network Based on Functional Connectivity"],"prefix":"10.1142","volume":"31","author":[{"given":"Naishi","family":"Feng","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fo","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, P. R. 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