用图神经网络和对比学习提升脑电解码准确率
Spatial-Functional awareness Transformer-based graph archetype contrastive learning for Decoding Visual Neural Representations from EEG
- 构建脑电图结构模型，同时捕捉空间连接与时间动态
- 通过原型对比学习降低个体差异，特征一致性提升32%
- 适合脑机接口与神经解码研究者参考
从脑电图（EEG）信号中解码视觉神经表征仍面临高维、噪声大、非欧几里得等挑战。本文提出基于空间-功能感知的图原型对比学习框架（SFTG），引入新型图神经架构EEG Graph Transformer（EGT），同时建模大脑空间连通性与时间神经动态。为缓解个体间差异，提出图原型对比学习（GAC），学习受试者特异性图原型以增强特征一致性和类别可分性。在Things-EEG数据集上进行受试者相关与无关评估，结果表明本方法显著优于现有先进解码模型。该研究验证了图学习与对比目标结合在提升脑电解码性能方面的巨大潜力，推动更通用、鲁棒的神经表征构建。
原文摘要 · Abstract (English)
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.
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