用自监督几何建模提升神经退行性疾病脑电解码准确率
EEG-ReMinD: Enhancing Neurodegenerative EEG Decoding through Self-Supervised State Reconstruction-Primed Riemannian Dynamics
- 通过自监督重建引导的黎曼动态建模，挖掘脑电信号内在几何结构
- 在完整与损坏数据上均显著优于对比方法，提升疾病分类性能
- 适合脑机接口与神经疾病诊断领域研究者参考
脑电图（EEG）解码算法面临数据稀疏、受试者差异大及标注依赖高等挑战，制约脑机接口发展和疾病诊断精度。为此，我们提出一种名为自监督状态重建引导的黎曼动力学（EEG-ReMinD）的两阶段新方法，减少对监督学习的依赖，并融合信号固有的几何特征。该方法结合自监督学习、几何学习与注意力机制，在黎曼流形框架下分析脑电特征的时序动态，称为黎曼动力学。在两种不同神经退行性疾病的数据集上，无论数据是否被污染，实验均验证了EEG-ReMinD的优越性能。
原文摘要 · Abstract (English)
The development of EEG decoding algorithms confronts challenges such as data sparsity, subject variability, and the need for precise annotations, all of which are vital for advancing brain-computer interfaces and enhancing the diagnosis of diseases. To address these issues, we propose a novel two-stage approach named Self-Supervised State Reconstruction-Primed Riemannian Dynamics (EEG-ReMinD) , which mitigates reliance on supervised learning and integrates inherent geometric features. This approach efficiently handles EEG data corruptions and reduces the dependency on labels. EEG-ReMinD utilizes self-supervised and geometric learning techniques, along with an attention mechanism, to analyze the temporal dynamics of EEG features within the framework of Riemannian geometry, referred to as Riemannian dynamics. Comparative analyses on both intact and corrupted datasets from two different neurodegenerative disorders underscore the enhanced performance of EEG-ReMinD.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。