用于脑机接口的事件相关（去）同步变异性量化：一个统一且可解释的框架

中文摘要 AI 辅助

目的：脑机接口（BCI）通过从脑电图（EEG）中解码用户意图来控制外部设备。然而，用户内部和之间的大量EEG变异性仍然是一个主要挑战。为了更好地理解这种变异性，我们提出了可解释的指标，独立量化用户内部和之间BCI相关脑活动的时间、空间和频率变异性。方法：我们提出一个框架，通过提取EEG特征并使用适当的距离函数将变异性定义为其围绕质心的离散度来量化变异性。使用两个运动想象BCI数据集（N = 133用户），我们通过用户内部和跨用户分类实验研究了BCI性能与变异性指标之间的关系。结果：在大多数条件下观察到-0.2至-0.4的负相关，表明较低的变异性与较高的BCI性能相关。此外，这些指标揭示了深度学习和基于黎曼几何的分类器在对变异性的鲁棒性方面的差异，前者显示出较弱的相关性。结论：结果证明了所提出的变异性指标的有效性，并表明降低变异性可能提高BCI性能，同时揭示分类模型对不同类型变异性的敏感性差异。意义：该框架在多个层次水平（试验内、试验间和试验间组）量化时间、空间和频率变异性，提供可解释的措施以更好地理解EEG变异性并支持更强大的BCI。它还可用于表征数据集变异性、评估分类器敏感性、将变异性纳入目标函数并提供基于变异性的用户反馈。

英文摘要

Objective: Brain-Computer Interfaces (BCIs) enable the control of external devices by decoding user intentions from electroencephalography (EEG). However, substantial EEG variability within and between users remains a major challenge. To better understand this variability, we propose interpretable metrics that independently quantify temporal, spatial, and frequency variability in BCI related brain activity within and between users. Methods: We propose a framework to quantify variability by extracting EEG features and defining variability as their dispersion around their centroid using appropriate distance functions. Using two motor imagery BCI datasets (N = 133 users), we investigated the relationship between BCI performance and the variability metrics through within-user and cross-user classification experiments. Results: Negative correlations of -0.2 to -0.4 were observed across most conditions, suggesting that lower variability is associated with higher BCI performance. Moreover, the metrics revealed differences in robustness to variability between the deep learning and Riemannian-based classifiers, with the former showing weaker correlations. Conclusion: The results demonstrate the effectiveness of the proposed variability metrics and suggest that reducing variability may improve BCI performance while revealing differences in the sensitivity of classification models to different types of variability. Significance: The framework quantifies temporal, spatial, and frequency variability at multiple hierarchical levels (within-trial, between-trial, and between-trial-group), providing interpretable measures to better understand EEG variability and support more robust BCIs. It could also be used to characterize dataset variability, evaluate classifier sensitivity, incorporate variability into objective functions, and provide variability-based user feedback.

发表机构

• Inria Centre at the University of Bordeaux（波尔多大学Inria中心）

机构由 AI 辅助整理，请以论文原文为准。