Tangent space spatial filters for interpretable and efficient Riemannian classification

TL;DR

This paper introduces a novel method for computing Riemannian tangent space spatial filters that enhances interpretability and efficiency in brain-computer interface classification, while enabling artifact removal and addressing high-dimensional challenges.

Contribution

It presents a new approach for deriving spatial filters from Riemannian tangent spaces, improving interpretability, efficiency, and artifact removal in BCI classification.

Findings

Proves a fundamental relationship between tangent spaces and spatial filtering.

Demonstrates improved classification efficiency and artifact removal.

Validates the method using an open-access BCI framework.

Abstract

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.