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

• URL: http://arxiv.org/abs/2512.07820v1
• Date: Mon, 08 Dec 2025 18:54:11 GMT
• Title: Graph-Based Learning of Spectro-Topographical EEG Representations with Gradient Alignment for Brain-Computer Interfaces
• Authors: Prithila Angkan, Amin Jalali, Paul Hungler, Ali Etemad,
• Abstract summary: We present a novel graph-based learning of EEG representations with gradient alignment (GEEGA)<n>Our model leverages graph convolutional networks to fuse embeddings from frequency-based topographical maps and time-frequency spectrograms.<n>We validate the efficacy of our method through extensive experiments on three publicly available EEG datasets.
• Score: 31.418253191692756
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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