Graph-Based Learning of Spectro-Topographical EEG Representations with Gradient Alignment for Brain-Computer Interfaces

TL;DR

This paper introduces GEEGA, a graph-based EEG representation learning method that combines multi-domain information with gradient alignment to improve brain-computer interface performance.

Contribution

The paper proposes a novel graph convolutional network approach with gradient alignment for multi-domain EEG feature fusion in BCI applications.

Findings

GEEGA outperforms existing methods on three EEG datasets.

Gradient alignment improves training stability and model accuracy.

Ablation studies confirm the effectiveness of each component.

Abstract

We present a novel graph-based learning of EEG representations with gradient alignment (GEEGA) that leverages multi-domain information to learn EEG representations for brain-computer interfaces. Our model leverages graph convolutional networks to fuse embeddings from frequency-based topographical maps and time-frequency spectrograms, capturing inter-domain relationships. GEEGA addresses the challenge of achieving high inter-class separability, which arises from the temporally dynamic and subject-sensitive nature of EEG signals by incorporating the center loss and pairwise difference loss. Additionally, GEEGA incorporates a gradient alignment strategy to resolve conflicts between gradients from different domains and the fused embeddings, ensuring that discrepancies, where gradients point in conflicting directions, are aligned toward a unified optimization direction. We validate the efficacy of our method through extensive experiments on three publicly available EEG datasets: BCI-2a, CL-Drive and CLARE. Comprehensive ablation studies further highlight the impact of various components of our model.