用于可解释和有效的黎曼分类的切空间空间滤波器
Tangent space spatial filters for interpretable and efficient Riemannian classification
摘要：
基于黎曼几何的方法已被证明是脑机接口（BCI）解码的良好模型。然而，这些方法的一个主要缺点是，不可能确定分类器建立在信号的哪个方面，从而留下了伪影驱动分类性能的可能性。在人工控制存在问题的领域，特别是患者人群中的神经反馈和脑机接口，这导致人们继续依赖空间滤波器作为生成可证明与大脑相关的特征的一种方式。此外，这些方法也受到维数灾难的影响，并且在高密度在线BCI系统中几乎是不可行的。为了解决这些缺点，我们在这里介绍了一种方法，用于计算空间滤波器从任何线性函数在黎曼正切空间，这使得更有效的分类，以及去除伪影源的分类建立在黎曼方法。我们首先证明了一定的切线空间和空间滤波方法之间的基本关系，包括在这个框架内的常见空间模式的解释，然后验证我们提出的方法使用开放访问的BCI分析框架。
英文摘要：
Methods based on Riemannian geometry have proven themselves to be good models for decoding in brain-computer interfacing (BCI). However, one major drawback of these methods is that it is not possible to determine what aspect of the signal the classifier is built on, leaving open the possibility that artifacts drive classification performance. In areas where artifactual control is problematic, specifically neurofeedback and BCIs in patient populations, this has led people to continue to rely on spatial filters as a way of generating features that are provably brain-related. Furthermore, these methods also suffer from the curse of dimensionality and are almost infeasible in high-density online BCI systems. To tackle these drawbacks, we introduce here a method for computing spatial filters from any linear function in the Riemannian tangent space, which allows for more efficient classification as well as the removal of artifact sources from classifiers built on Riemannian methods. We first prove a fundamental relationship between certain tangent spaces and spatial filtering methods, including an explanation of common spatial patterns within this framework, and then validate our proposed approach using an open-access BCI analysis framework.
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