The Riemannian Potato: an automatic and adaptive artifact detection method for online experiments using Riemannian geometry

Alexandre Barachant 1, * Anton Andreev 2 Marco Congedo 1
* Corresponding author
1 GIPSA-VIBS - VIBS
GIPSA-DIS - Département Images et Signal
2 GIPSA-Services - GIPSA-Services
GIPSA-lab - Grenoble Images Parole Signal Automatique
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.
Document type :
Conference papers
TOBI Workshop lV, Jan 2013, Sion, Switzerland. pp.19-20, 2013


https://hal.archives-ouvertes.fr/hal-00781701
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

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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>

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