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Low dimensional representations of MEG/EEG data using Laplacian Eigenmaps

Alexandre Gramfort 1 Maureen Clerc 1 
1 ODYSSEE - Computer and biological vision
DI-ENS - Département d'informatique - ENS Paris, CRISAM - Inria Sophia Antipolis - Méditerranée , ENS-PSL - École normale supérieure - Paris, Inria Paris-Rocquencourt, ENPC - École des Ponts ParisTech
Abstract : Magneto-encephalography (MEG) and electro-encephalograhy (EEG) experiments provide huge amounts of data and lead to the manipulations of high dimensional objects like time series or topographies. In the past, essentially in the last decade, various methods for extracting the structure in complex data have been developed and successfully exploited for visualization or classification purposes. Here we propose to use one of these methods, the Laplacian eigenmaps, on EEG data and prove that it provides an powerful approach to visualize and understand the underlying structure of evoked potentials or multitrial time series.
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Submitted on : Thursday, July 15, 2010 - 3:38:54 PM
Last modification on : Thursday, March 17, 2022 - 10:08:29 AM
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Alexandre Gramfort, Maureen Clerc. Low dimensional representations of MEG/EEG data using Laplacian Eigenmaps. Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging, 2007. NFSI-ICFBI 2007. Joint Meeting of the 6th International Symposium on, Oct 2007, Hangzhou, China. pp.169 - 172, ⟨10.1109/NFSI-ICFBI.2007.4387717⟩. ⟨inria-00502735⟩



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