PGCN: Pyramidal Graph Convolutional Network for EEG Emotion Recognition
South China University of Technology · Chinese Academy of Sciences · +3 more institutions
Abstract
Emotion recognition is essential in the diagnosis and rehabilitation of various mental diseases. In the last decade, electroencephalogram (EEG)-based emotion recognition has been intensively investigated due to its prominative accuracy and reliability, and graph convolutional network (GCN) has become a mainstream model to decode emotions from EEG signals. However, the electrode relationship, especially long-range electrode dependencies across the scalp, may be underutilized by GCNs, although such relationships have been proven to be important in emotion recognition. The small receptive field makes shallow GCNs only aggregate local nodes. On the other hand, stacking too many layers leads to over-smoothing. To…
Citation impact
- FWCI
- 34.16
- Percentile
- 100%
- References
- 60
Authors
5Topics & keywords
- Computer science
- Electroencephalography
- Pattern recognition (psychology)
- Artificial intelligence
- Convolutional neural network
- Graph
- Speech recognition
- Theoretical computer science