Spatial-Functional awareness Transformer-based graph archetype contrastive learning for Decoding Visual Neural Representations from EEG
This work addresses the problem of improving EEG-based visual decoding for brain-computer interfaces or neuroscience research, representing an incremental advancement by integrating graph-based learning with contrastive objectives.
The authors tackled the challenge of decoding visual neural representations from noisy, high-dimensional EEG signals by proposing a Spatial-Functional Awareness Transformer-based Graph Archetype Contrastive Learning (SFTG) framework, which significantly outperformed prior state-of-the-art EEG decoding methods in evaluations on the Things-EEG dataset.
Decoding visual neural representations from Electroencephalography (EEG) signals remains a formidable challenge due to their high-dimensional, noisy, and non-Euclidean nature. In this work, we propose a Spatial-Functional Awareness Transformer-based Graph Archetype Contrastive Learning (SFTG) framework to enhance EEG-based visual decoding. Specifically, we introduce the EEG Graph Transformer (EGT), a novel graph-based neural architecture that simultaneously encodes spatial brain connectivity and temporal neural dynamics. To mitigate high intra-subject variability, we propose Graph Archetype Contrastive Learning (GAC), which learns subject-specific EEG graph archetypes to improve feature consistency and class separability. Furthermore, we conduct comprehensive subject-dependent and subject-independent evaluations on the Things-EEG dataset, demonstrating that our approach significantly outperforms prior state-of-the-art EEG decoding methods.The results underscore the transformative potential of integrating graph-based learning with contrastive objectives to enhance EEG-based brain decoding, paving the way for more generalizable and robust neural representations.