Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadata

https://hal.inria.fr/inria-00502735
Contributor : Alexandre Gramfort Connect in order to contact the contributor
Submitted on : Thursday, July 15, 2010 - 3:38:54 PM
Last modification on : Thursday, March 17, 2022 - 10:08:29 AM
Long-term archiving on: : Tuesday, October 23, 2012 - 10:25:30 AM

File

GramfortNFSI2007_submitted.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

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⟩

Share

Metrics

Record views

120

Files downloads

381