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.

翻译：脑电图（EEG）分类是神经科学、神经工程及若干商业应用中的关键任务。然而，传统的EEG分类模型往往忽略或未能充分利用大脑的拓扑信息。针对这一不足，近年来研究者对利用图神经网络（GNN）的潜力产生浓厚兴趣，通过将每个EEG通道选定的特征建模为图结构，以挖掘拓扑信息。为促进该方向的研究，我们推出GNN4EEG——一个面向GNN建模EEG信号的通用且用户友好的工具包。GNN4EEG包含三个组成部分：(i)基于123名参与者EEG数据构建的、涵盖四项EEG分类任务的大型基准；(ii)多种先进GNN类EEG分类模型（如DGCNN、RGNN等）的易用实现；(iii)全面实验设置与评估方案（如数据分割协议和交叉验证协议）的实现。GNN4EEG已公开发布于https://github.com/Miracle-2001/GNN4EEG。