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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