GNN4EEG: A Benchmark and Toolkit for Electroencephalography
  Classification with Graph Neural Network
Kaiyuan Zhang Ziyi Ye Qingyao Ai Xiaohui Xie Yiqun Liu
Electroencephalography(EEG) classification is a crucial task in neuroscience,
neural engineering, and several commercial applications. Traditional EEG
classification models, however, have often overlooked or inadequately leveraged
the brain's topological information. Recognizing this shortfall, there has been
a burgeoning interest in recent years in harnessing the potential of Graph
Neural Networks (GNN) to exploit the topological information by modeling
features selected from each EEG channel in a graph structure. To further
facilitate research in this direction, we introduce GNN4EEG, a versatile and
user-friendly toolkit for GNN-based modeling of EEG signals. GNN4EEG comprises
three components: (i)A large benchmark constructed with four EEG classification
tasks based on EEG data collected from 123 participants. (ii)Easy-to-use
implementations on various state-of-the-art GNN-based EEG classification
models, e.g., DGCNN, RGNN, etc. (iii)Implementations of comprehensive
experimental settings and evaluation protocols, e.g., data splitting protocols,
and cross-validation protocols. GNN4EEG is publicly released at
https://github.com/Miracle-2001/GNN4EEG.