DeepAI
Log In Sign Up

Using Riemannian geometry for SSVEP-based Brain Computer Interface

01/14/2015
by   Emmanuel K. Kalunga, et al.
0

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.

READ FULL TEXT

page 19

page 20

06/04/2019

Manifold-regression to predict from MEG/EEG brain signals without source modeling

Magnetoencephalography and electroencephalography (M/EEG) can reveal neu...
10/19/2021

Riemannian classification of EEG signals with missing values

This paper proposes two strategies to handle missing data for the classi...
02/09/2021

Clinical BCI Challenge-WCCI2020: RIGOLETTO – RIemannian GeOmetry LEarning, applicaTion To cOnnectivity

This short technical report describes the approach submitted to the Clin...
12/01/2017

Subject Selection on a Riemannian Manifold for Unsupervised Cross-subject Seizure Detection

Inter-subject variability between individuals poses a challenge in inter...
07/17/2022

Fusion of Physiological and Behavioural Signals on SPD Manifolds with Application to Stress and Pain Detection

Existing multimodal stress/pain recognition approaches generally extract...
02/11/2019

Global Perturbation of Initial Geometry in a Biomechanical Model of Cortical Morphogenesis

Cortical folding pattern is a main characteristic of the geometry of the...
09/23/2019

Tangent space spatial filters for interpretable and efficient Riemannian classification

Methods based on Riemannian geometry have proven themselves to be good m...

Code Repositories

Online-SSVEP

Matlab codes for the Online SSVEP-based BCI using Riemannian Geometry algorithm


view repo