Distance Covariance: A Nonlinear Extension of Riemannian Geometry for EEG-Based Brain-Computer Interfacing

Distance Covariance: A Nonlinear Extension of Riemannian Geometry for EEG-Based Brain-Computer Interfacing

Abstract

Riemannian frameworks are the basis for some of the best-performing decoding methods in EEG-based Brain-Computer Interfacing. In this work, we consider whether a nonlinear extension of the Riemannian framework, obtained by replacing the channel-wise covariance of the EEG signal with the nonlinear distance covariance, improves decoding performance. We study the theoretical properties of the distance covariance metric in this framework, in particular invariance to affine transformations, and compare the proposed method with established Riemannian methods on three different EEG data sets. We do not find evidence that the distance covariance extension improves decoding performance in comparison to the linear Riemannian framework

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Authors
  • Xu, Jiachen
  • Markham, Alex
  • Meunier, Anja
  • Raggam, Philipp
  • Grosse-Wentrup, Moritz
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Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Paper)
Event Title
2021 IEEE International Conference on Systems, Man, and Cybernetics
Divisions
Neuroinformatics
Event Location
virtual
Event Type
Conference
Event Dates
17-20 Oct 2021
Date
17 October 2021
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