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

TL;DR

GNN4EEG is a comprehensive benchmark and toolkit that leverages graph neural networks to improve EEG classification by utilizing brain topological information, facilitating research and development in this field.

Contribution

It introduces a versatile toolkit with benchmark datasets, implementations of state-of-the-art GNN models, and standardized evaluation protocols for EEG classification.

Findings

Provides a large EEG classification benchmark with data from 123 participants.

Includes implementations of GNN models like DGCNN and RGNN for EEG analysis.

Offers standardized experimental and evaluation protocols for reproducible research.

Abstract

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.