EEG_GLT-Net: Optimising EEG Graphs for Real-time Motor Imagery Signals
  Classification
Htoo Wai Aung Jiao Jiao Li Yang An Steven W. Su
Brain-Computer Interfaces connect the brain to external control devices,
necessitating the accurate translation of brain signals such as from
electroencephalography (EEG) into executable commands. Graph Neural Networks
(GCN) have been increasingly applied for classifying EEG Motor Imagery signals,
primarily because they incorporates the spatial relationships among EEG
channels, resulting in improved accuracy over traditional convolutional
methods. Recent advances by GCNs-Net in real-time EEG MI signal classification
utilised Pearson Coefficient Correlation (PCC) for constructing adjacency
matrices, yielding significant results on the PhysioNet dataset. Our paper
introduces the EEG Graph Lottery Ticket (EEG_GLT) algorithm, an innovative
technique for constructing adjacency matrices for EEG channels. It does not
require pre-existing knowledge of inter-channel relationships, and it can be
tailored to suit both individual subjects and GCN model architectures. Our
findings demonstrated that the PCC method outperformed the Geodesic approach by
9.65% in mean accuracy, while our EEG_GLT matrix consistently exceeded the
performance of the PCC method by a mean accuracy of 13.39%. Also, we found that
the construction of the adjacency matrix significantly influenced accuracy, to
a greater extent than GCN model configurations. A basic GCN configuration
utilising our EEG_GLT matrix exceeded the performance of even the most complex
GCN setup with a PCC matrix in average accuracy. Our EEG_GLT method also
reduced MACs by up to 97% compared to the PCC method, while maintaining or
enhancing accuracy. In conclusion, the EEG_GLT algorithm marks a breakthrough
in the development of optimal adjacency matrices, effectively boosting both
computational accuracy and efficiency, making it well-suited for real-time
classification of EEG MI signals that demand intensive computational resources.