HyperGCN: Interpreting Hyperscanning EEG Signals for Common Multi-Task Classification Using Graph Convolutional Networks

EEG hyperscanning employs electroencephalography (EEG) activity to simultaneously monitor the brain activities of several individuals as they interact. During hyperscanning research, scientists pay attention to how brain activities of two or more subject become synchronized while doing a task. However, technological limitations associated with EEG hyperscanning-based brain-computer interfaces (BCIs) slowed down the exploration of this research for rehabilitation purposes. The method HyperCSP aims to minimize the impact of irrelevant actions that participants might perform naturally or deliberately. It effectively isolates a common motor task shared among several individuals. Recognizing this challenge, we developed HyperGCN, which identifies connectivity between the subjects' channels during the multi-common task. HyperGCN utilizes the principles of graph theory to analyze complex networks of brain activity. This allows a sophisticated interpretation of intra-brain connectivity. The HyperGCN helps to understand the patterns and interactions that traditional analysis methods miss by treating these connections as graphs. This method achieved an average resulting accuracy of 92.86% on the hyperscanning dataset. All code is publicly available on github<xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">¹</xref><fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label>¹</label> <uri>https://github.com/abduloo7454/XAI-Hyper-GCN/</uri> </fn>