从视频到EEG：适应联合嵌入预测架构以揭示脑信号分析中的空间时间动态
From Video to EEG: Adapting Joint Embedding Predictive Architecture to Uncover Saptiotemporal Dynamics in Brain Signal Analysis
AI总结：
本文提出EEG-VJEPA，通过将EEG视为视频序列，利用联合嵌入和自适应掩码学习空间时间表示，提升分类准确率并提供可解释的嵌入，适用于实际临床场景。
摘要：
EEG信号以高时间分辨率和低空间分辨率捕捉脑活动，支持神经诊断、认知监测和脑机接口应用。然而，有效分析受到标注数据有限、高维性和缺乏能完全捕捉空间时间依赖性的可扩展模型的阻碍。现有自监督学习方法往往侧重于空间或时间特征，导致表示不优。为此，我们提出EEG-VJEPA，一种针对EEG分类的V-JEPA新适应。通过将EEG视为视频序列，EEG-VJEPA利用联合嵌入和自适应掩码学习语义丰富的空间时间表示。评估表明，EEG-VJEPA在公开的Temple University Hospital（TUH）异常EEG数据集上优于现有最先进模型。除了分类准确率外，EEG-VJEPA捕捉到生理相关的时间和空间信号模式，提供可解释的嵌入，可能支持人机协作的诊断流程。这些发现使EEG-VJEPA成为可扩展、可信的EEG分析框架，适用于真实临床环境。
英文摘要：
EEG signals capture brain activity with high temporal and low spatial resolution, supporting applications such as neurological diagnosis, cognitive monitoring, and brain-computer interfaces. However, effective analysis is hindered by limited labeled data, high dimensionality, and the absence of scalable models that fully capture spatiotemporal dependencies. Existing self-supervised learning (SSL) methods often focus on either spatial or temporal features, leading to suboptimal representations. To this end, we propose EEG-VJEPA, a novel adaptation of the Video Joint Embedding Predictive Architecture (V-JEPA) for EEG classification. By treating EEG as video-like sequences, EEG-VJEPA learns semantically meaningful spatiotemporal representations using joint embeddings and adaptive masking. To our knowledge, this is the first work that exploits V-JEPA for EEG classification and explores the visual concepts learned by the model. Evaluations on the publicly available Temple University Hospital (TUH) Abnormal EEG dataset show that EEG-VJEPA outperforms existing state-of-the-art models in classification accuracy. Beyond classification accuracy, EEG-VJEPA captures physiologically relevant spatial and temporal signal patterns, offering interpretable embeddings that may support human-AI collaboration in diagnostic workflows. These findings position EEG-VJEPA as a promising framework for scalable, trustworthy EEG analysis in real-world clinical settings.
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。
