基于空间-功能感知Transformer的图原型对比学习用于从EEG解码视觉神经表征
Spatial-Functional awareness Transformer-based graph archetype contrastive learning for Decoding Visual Neural Representations from EEG
AI总结：
提出SFTG框架，结合EEG图Transformer与图原型对比学习，在Things-EEG数据集上显著提升视觉解码性能，优于现有最先进方法。
摘要：
从脑电图（EEG）信号中解码视觉神经表征仍然是一项艰巨的挑战，这归因于其高维、嘈杂且非欧几里得的特性。在本工作中，我们提出了一种基于空间-功能感知Transformer的图原型对比学习（SFTG）框架，以增强基于EEG的视觉解码。具体而言，我们引入了EEG图Transformer（EGT），这是一种新颖的基于图的神经架构，能够同时编码空间大脑连接性和时间神经动态。为缓解高受试者内变异性，我们提出了图原型对比学习（GAC），该学习机制学习受试者特定的EEG图原型，以提高特征一致性和类别可分性。此外，我们在Things-EEG数据集上进行了全面的受试者依赖和受试者独立评估，结果表明我们的方法显著优于先前最先进的EEG解码方法。这些结果凸显了将基于图的学习与对比目标相结合以增强基于EEG的大脑解码的变革潜力，为更通用和稳健的神经表征铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
