GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals

Towards developing effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by electroencephalogram (EEG), is highly demanded. Traditional works classify EEG signals without considering the topological relationship among electrodes. However, neurosc...

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Veröffentlicht in:	arXiv.org
Hauptverfasser:	Hou, Yimin, Jia, Shuyue, Lun, Xiangmin, Ziqian Hao, Shi, Yan, Yang, Li, Zeng, Rui, Lv, Jinglei
Format:	Paper
Sprache:	Englisch
Veröffentlicht:	Ithaca Cornell University Library, arXiv.org 26.08.2022
Schlagworte:	Artificial neural networks Datasets Electrodes Electroencephalography Human-computer interface Imagery Machine learning Neural networks Signal classification System effectiveness Topology
ISSN:	2331-8422
Online-Zugang:	Volltext
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