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Electroencephalography(EEG) classification is a crucial task in neuroscience,neural engineering, and several commercial applications. Traditional EEGclassification models, however, have often overlooked or inadequately leveragedthe brain's topological information. Recognizing this shortfall, there has beena burgeoning interest in recent years in harnessing the potential of GraphNeural Networks (GNN) to exploit the topological information by modelingfeatures selected from each EEG channel in a graph structure. To furtherfacilitate research in this direction, we introduce GNN4EEG, a versatile anduser-friendly toolkit for GNN-based modeling of EEG signals. GNN4EEG comprisesthree components: (i)A large benchmark constructed with four EEG classificationtasks based on EEG data collected from 123 participants. (ii)Easy-to-useimplementations on various state-of-the-art GNN-based EEG classificationmodels, e.g., DGCNN, RGNN, etc. (iii)Implementations of comprehensiveexperimental settings and evaluation protocols, e.g., data splitting protocols,and cross-validation protocols. GNN4EEG is publicly released athttps://github.com/Miracle-2001/GNN4EEG.

GNN4EEG: A Benchmark and Toolkit for Electroencephalography Classification with Graph Neural Network