  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 solve these problems, we propose the pyramidal
graph convolutional network (PGCN), which aggregates features at three levels:
local, mesoscopic, and global. First, we construct a vanilla GCN based on the
3D topological relationships of electrodes, which is used to integrate
two-order local features; Second, we construct several mesoscopic brain regions
based on priori knowledge and employ mesoscopic attention to sequentially
calculate the virtual mesoscopic centers to focus on the functional connections
of mesoscopic brain regions; Finally, we fuse the node features and their 3D
positions to construct a numerical relationship adjacency matrix to integrate
structural and functional connections from the global perspective. Experimental
results on three public datasets indicate that PGCN enhances the relationship
modelling across the scalp and achieves state-of-the-art performance in both
subject-dependent and subject-independent scenarios. Meanwhile, PGCN makes an
effective trade-off between enhancing network depth and receptive fields while
suppressing the ensuing over-smoothing. Our codes are publicly accessible at
https://github.com/Jinminbox/PGCN.
