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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Veröffentlicht in:	Neurocomputing (Amsterdam) Jg. 191; S. 55 - 68
Hauptverfasser:	Kalunga, Emmanuel K., Chevallier, Sylvain, Barthélemy, Quentin, Djouani, Karim, Monacelli, Eric, Hamam, Yskandar
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 26.05.2016 Elsevier
Schlagworte:	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-Zugang:	Volltext
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Abstract	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.
AbstractList	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 Rie-mannian 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. 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. 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.
Author	Monacelli, Eric Chevallier, Sylvain Djouani, Karim Kalunga, Emmanuel K. Barthélemy, Quentin Hamam, Yskandar
Author_xml	– sequence: 1 givenname: Emmanuel K. surname: Kalunga fullname: Kalunga, Emmanuel K. organization: Department of Electrical Engineering and the French South African Institute of Technology, Tshwane University of Technology, Pretoria 0001, South Africa – sequence: 2 givenname: Sylvain orcidid: 0000-0003-3027-8241 surname: Chevallier fullname: Chevallier, Sylvain organization: Laboratoire d׳Ingénierie des Systèmes de Versailles, Université de Versailles Saint-Quentin, 78140 Velizy, France – sequence: 3 givenname: Quentin surname: Barthélemy fullname: Barthélemy, Quentin organization: Mensia Technologies, S.A. ICM, Hôpital de la Pitié-Salpêtrière, 75013 Paris, France – sequence: 4 givenname: Karim surname: Djouani fullname: Djouani, Karim organization: Department of Electrical Engineering and the French South African Institute of Technology, Tshwane University of Technology, Pretoria 0001, South Africa – sequence: 5 givenname: Eric surname: Monacelli fullname: Monacelli, Eric organization: Laboratoire d׳Ingénierie des Systèmes de Versailles, Université de Versailles Saint-Quentin, 78140 Velizy, France – sequence: 6 givenname: Yskandar surname: Hamam fullname: Hamam, Yskandar organization: Department of Electrical Engineering and the French South African Institute of Technology, Tshwane University of Technology, Pretoria 0001, South Africa
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Keywords	Riemannian geometry Asynchronous Online Steady State Visually Evoked Potentials Brain–Computer Interfaces Brain-Computer Interfaces Steady State Visually-Evoked Potential Riemannian Geometry
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SubjectTerms	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
Title	Online SSVEP-based BCI using Riemannian geometry
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