Online SSVEP-based BCI using Riemannian geometry

Challenges for the next generation of Brain Computer Interfaces (BCI) are to mitigate the common sources of variability (electronic, electrical, biological) and to develop online and adaptive systems following the evolution of the subject׳s brain waves. Studying electroencephalographic (EEG) signals...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) Vol. 191; pp. 55 - 68
Main Authors: Kalunga, Emmanuel K., Chevallier, Sylvain, Barthélemy, Quentin, Djouani, Karim, Monacelli, Eric, Hamam, Yskandar
Format: Journal Article
Language:English
Published: Elsevier B.V 26.05.2016
Elsevier
Subjects:
Algorithms
Asynchronous
Brain
Brain–Computer Interfaces
Classification
Cognitive science
Computer Science
Covariance
Electroencephalography
Electronics
Human-computer interface
Machine Learning
Neural and Evolutionary Computing
Neuroscience
Online
Recording
Riemannian geometry
Signal and Image Processing
Steady State Visually Evoked Potentials
Riemannian geometry
Asynchronous
Online
Steady State Visually Evoked Potentials
Brain–Computer Interfaces
Brain-Computer Interfaces
Steady State Visually-Evoked Potential
Riemannian Geometry
ISSN:0925-2312, 1872-8286
Online Access:Get full text
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Summary:Challenges for the next generation of Brain Computer Interfaces (BCI) are to mitigate the common sources of variability (electronic, electrical, biological) and to develop online and adaptive systems following the evolution of the subject׳s brain waves. Studying electroencephalographic (EEG) signals from their associated covariance matrices allows the construction of a representation which is invariant to extrinsic perturbations. As covariance matrices should be estimated, this paper first presents a thorough study of all estimators conducted on real EEG recording. Working in Euclidean space with covariance matrices is known to be error-prone, one might take advantage of algorithmic advances in Riemannian geometry and matrix manifold to implement methods for Symmetric Positive-Definite (SPD) matrices. Nonetheless, existing classification algorithms in Riemannian spaces are designed for offline analysis. We propose a novel algorithm for online and asynchronous processing of brain signals, borrowing principles from semi-unsupervised approaches and following a dynamic stopping scheme to provide a prediction as soon as possible. The assessment is conducted on real EEG recording: this is the first study on Steady-State Visually Evoked Potential (SSVEP) experimentations to exploit online classification based on Riemannian geometry. The proposed online algorithm is evaluated and compared with state-of-the-art SSVEP methods, which are based on Canonical Correlation Analysis (CCA). It is shown to improve both the classification accuracy and the information transfer rate in the online and asynchronous setup.
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ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2016.01.007