Averaging covariance matrices for EEG signal classification based on the CSP: An empirical study

This paper presents an empirical comparison of covariance matrix averaging methods for EEG signal classification. Indeed, averaging EEG signal covariance matrices is a key step in designing brain-computer interfaces (BCI) based on the popular common spatial pattern (CSP) algorithm. BCI paradigms are...

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Bibliographic Details

Published in:	2015 23rd European Signal Processing Conference (EUSIPCO) pp. 2721 - 2725
Main Authors:	Yger, Florian, Lotte, Fabien, Sugiyama, Masashi
Format:	Conference Proceeding
Language:	English
Published:	EURASIP 01.08.2015
Subjects:	brain-computer interface (BCI) common spatial pattern Covariance matrices EEG signal classification Electroencephalography Europe Feature extraction Geometry Riemannian geometry robust averaging Signal processing SPD matrices Symmetric matrices
ISSN:	2076-1465
Online Access:	Get full text
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