GEFM: Graph-Enhanced EEG Foundation Model
This work addresses the problem of limited labeled EEG data for disease diagnosis and healthcare applications, representing an incremental improvement by incorporating inter-channel relationships into existing foundation model approaches.
The paper tackled the challenge of labeled EEG data scarcity by proposing GEFM, a foundation model that integrates temporal and inter-channel information using Graph Neural Networks and a masked autoencoder, achieving consistent outperformance over baselines across three downstream tasks.
Electroencephalography (EEG) signals provide critical insights for applications in disease diagnosis and healthcare. However, the scarcity of labeled EEG data poses a significant challenge. Foundation models offer a promising solution by leveraging large-scale unlabeled data through pre-training, enabling strong performance across diverse tasks. While both temporal dynamics and inter-channel relationships are vital for understanding EEG signals, existing EEG foundation models primarily focus on the former, overlooking the latter. To address this limitation, we propose Graph-Enhanced EEG Foundation Model (GEFM), a novel foundation model for EEG that integrates both temporal and inter-channel information. Our architecture combines Graph Neural Networks (GNNs), which effectively capture relational structures, with a masked autoencoder to enable efficient pre-training. We evaluated our approach using three downstream tasks and experimented with various GNN architectures. The results demonstrate that our proposed model, particularly when employing the GCN architecture with optimized configurations, consistently outperformed baseline methods across all tasks. These findings suggest that our model serves as a robust foundation model for EEG analysis.