Graph Neural Networks#
Graph Neural Network
This category contains graph-based models that treat EEG electrodes as graph nodes and learn inter-channel relationships dynamically.
Available Models
DGCNN— Dynamic Graph Convolutional Neural Network. Treats electrodes as graph nodes and learns the adjacency matrix jointly with all other parameters via back-propagation. Uses Chebyshev spectral graph convolution to extract spatial features from the learned graph Laplacian. Based on Song et al. (2018).