用正交分解新方法建模脑肌信号关联，提升运动识别精度。
Learning Cortico-Muscular Dependence through Orthonormal Decomposition of Density Ratios
- 基于密度比正交分解，学习脑电与肌电的动态依赖关系。
- 提取的特征可准确分类动作和受试者，正确率达92%以上。
- 适合神经工程、脑机接口研究者，揭示信号通道与时间依赖性。
皮层-脊髓神经通路对运动控制至关重要，通常通过同步脑电图（EEG）与肌电图（EMG）记录研究。然而，现有方法在捕捉高层次上下文连通性方面存在局限。本文提出一种基于密度比正交分解的统计依赖估计新方法，用于建模皮层与肌肉振荡之间的关系。该方法从信号实现中学习密度比的特征值、特征函数及投影空间，解决了传统标量度量在可解释性、可扩展性和局部时序依赖性方面的不足。实验表明，所学特征函数能准确分类运动与受试者，且揭示特定脑电通道在运动期间的激活模式。代码已公开于 https://github.com/bohu615/corticomuscular-eigen-encoder。
原文摘要 · Abstract (English)
The cortico-spinal neural pathway is fundamental for motor control and movement execution, and in humans it is typically studied using concurrent electroencephalography (EEG) and electromyography (EMG) recordings. However, current approaches for capturing high-level and contextual connectivity between these recordings have important limitations. Here, we present a novel application of statistical dependence estimators based on orthonormal decomposition of density ratios to model the relationship between cortical and muscle oscillations. Our method extends from traditional scalar-valued measures by learning eigenvalues, eigenfunctions, and projection spaces of density ratios from realizations of the signal, addressing the interpretability, scalability, and local temporal dependence of cortico-muscular connectivity. We experimentally demonstrate that eigenfunctions learned from cortico-muscular connectivity can accurately classify movements and subjects. Moreover, they reveal channel and temporal dependencies that confirm the activation of specific EEG channels during movement. Our code is available at https://github.com/bohu615/corticomuscular-eigen-encoder.
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。