Using Riemannian geometry for SSVEP-based Brain Computer Interface

TL;DR

This paper explores the use of Riemannian geometry for classifying EEG signals in BCI, proposing an online Riemannian classifier for SSVEP, and evaluating various covariance estimators on real data.

Contribution

It introduces an online Riemannian classifier for EEG-based BCI and assesses different covariance estimators for improved signal classification.

Findings

Riemannian geometry improves EEG signal classification robustness.

The proposed online classifier performs effectively in SSVEP experiments.

Different covariance estimators impact classification accuracy.

Abstract

Riemannian geometry has been applied to Brain Computer Interface (BCI) for brain signals classification yielding promising results. Studying electroencephalographic (EEG) signals from their associated covariance matrices allows a mitigation of common sources of variability (electronic, electrical, biological) by constructing a representation which is invariant to these perturbations. While working in Euclidean space with covariance matrices is known to be error-prone, one might take advantage of algorithmic advances in information geometry and matrix manifold to implement methods for Symmetric Positive-Definite (SPD) matrices. This paper proposes a comprehensive review of the actual tools of information geometry and how they could be applied on covariance matrices of EEG. In practice, covariance matrices should be estimated, thus a thorough study of all estimators is conducted on real EEG dataset. As a main contribution, this paper proposes an online implementation of a classifier in the Riemannian space and its subsequent assessment in Steady-State Visually Evoked Potential (SSVEP) experimentations.