Abstract : Artifacts management is a critical problem in any applications involving on-line processing of EEG signals. This paper presents a multivariate automatic and adaptive method for identifying artifacts in continuous EEG data.
Contributor : Alexandre Barachant <>
Submitted on : Monday, January 28, 2013 - 11:01:44 AM
Last modification on : Monday, February 22, 2016 - 4:46:46 PM
Document(s) archivé(s) le : Monday, June 17, 2013 - 4:02:49 PM
Alexandre Barachant, Anton Andreev, Marco Congedo. The Riemannian Potato: an automatic and adaptive artifact detection method for online experiments using Riemannian geometry. TOBI Workshop lV, Jan 2013, Sion, Switzerland. pp.19-20, 2013. <hal-00781701>