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 nonlinea...Show MoreMetadata
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.
Date of Conference: 17-20 October 2021
Date Added to IEEE Xplore: 06 January 2022
ISBN Information:
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- IEEE Keywords
- Index Terms
- Riemannian Manifold ,
- Distance Covariance ,
- EEG Data ,
- Affine Transformation ,
- Decoding Performance ,
- EEG Dataset ,
- Classification Accuracy ,
- Support Vector Machine ,
- Covariance Matrix ,
- Distance Matrix ,
- Positive Definite Matrix ,
- Definite Matrix ,
- Spatial Filter ,
- Linear Classifier ,
- Type Of Matrix ,
- Linear Kernel ,
- Linearly Separable ,
- Polynomial Kernel ,
- Tangent Space ,
- Sample Covariance Matrix ,
- Nonlinear Framework ,
- Choice Of Classifier ,
- Kernel Support Vector Machine ,
- Nonlinear Projection ,
- Kernel Space ,
- Nonlinear Kernel ,
- Sample Covariance ,
- Motor Imagery Tasks ,
- Tangent Vector ,
- EEG Channels
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Riemannian Manifold ,
- Distance Covariance ,
- EEG Data ,
- Affine Transformation ,
- Decoding Performance ,
- EEG Dataset ,
- Classification Accuracy ,
- Support Vector Machine ,
- Covariance Matrix ,
- Distance Matrix ,
- Positive Definite Matrix ,
- Definite Matrix ,
- Spatial Filter ,
- Linear Classifier ,
- Type Of Matrix ,
- Linear Kernel ,
- Linearly Separable ,
- Polynomial Kernel ,
- Tangent Space ,
- Sample Covariance Matrix ,
- Nonlinear Framework ,
- Choice Of Classifier ,
- Kernel Support Vector Machine ,
- Nonlinear Projection ,
- Kernel Space ,
- Nonlinear Kernel ,
- Sample Covariance ,
- Motor Imagery Tasks ,
- Tangent Vector ,
- EEG Channels