EEG-GCN: Spatio-Temporal and Self-Adaptive Graph Convolutional Networks for Single and Multi-View EEG-Based Emotion Recognition

• Published in: IEEE signal processing letters Vol. 29; pp. 1574 - 1578
• Main Authors: Gao, Yue, Fu, Xiangling, Ouyang, Tianxiong, Wang, Yi
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Brain; Brain modeling; Convolution; EEG; Electroencephalography; Emotion recognition; Emotions; Feature extraction; Frequency-domain analysis; graph convolutional neural network; Network topology; Redundancy; spatio-temporal attention mechanism; Topology

Graph networks are naturally suitable for modeling multi-channel features of EEG signals. However, the existing study that attempts to utilize graph-based neural networks for EEG-based emotion recognition doesn't take the spatio-temporal redundancy of EEG features and differences in brain topol...