Weighted spatial based geometric scheme as an efficient algorithm for analyzing single-trial EEGS to improve cue-based BCI classification

There is a growing interest in analyzing the geometrical behavior of electroencephalogram (EEG) covariance matrix in the context of brain computer interface (BCI). The bottleneck of the current Riemannian framework is the bias of the mean vector of EEG signals to the noisy trials, which deteriorates...

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Bibliographic Details

Published in:	Neural networks Vol. 92; pp. 69 - 76
Main Authors:	Alimardani, Fatemeh, Boostani, Reza, Blankertz, Benjamin
Format:	Journal Article
Language:	English
Published:	United States Elsevier Ltd 01.08.2017
Subjects:	Algorithms Brain - physiology Brain-Computer Interfaces - classification Covariance matrix Cue-based Brain computer interface Cues Electroencephalography - methods Humans Riemannian geometry Weighting algorithm Riemannian geometry Weighting algorithm Covariance matrix Cue-based Brain computer interface
ISSN:	0893-6080, 1879-2782, 1879-2782
Online Access:	Get full text
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