creators_name: Vigário, Ricardo
creators_name: Särelä, Jaakko
creators_name: Jousmäki, Veikko
creators_name: Hämäläinen, Matti
creators_name: Oja, Erkki
creators_id: 
creators_id: 4715
creators_id: 
creators_id: 
creators_id: 
type: journalp
datestamp: 2004-05-24
lastmod: 2011-03-11 08:55:36
metadata_visibility: show
title: Independent component approach to the analysis of EEG and MEG recordings
ispublished: pub
subjects: brain-img
subjects: comp-sci-stat-model
subjects: comp-sci-mach-learn
full_text_status: public
keywords: independent component analysis (ICA), blind source    separation (BSS), unsupervised learning, electroencephalography (EEG), magnetoencephalography(MEG), artifact removal, auditory evoked field (AEF), somatosensory evoked field (SEF)

abstract: Multichannel recordings of the electromagnetic fields
emerging from neural currents in the brain generate large amounts
of data. Suitable feature extraction methods are, therefore, useful
to facilitate the representation and interpretation of the data.
Recently developed independent component analysis (ICA) has
been shown to be an efficient tool for artifact identification and
extraction from electroencephalographic (EEG) and magnetoen-
cephalographic (MEG) recordings. In addition, ICA has been ap-
plied to the analysis of brain signals evoked by sensory stimuli. This
paper reviews our recent results in this field.

date: 2000
date_type: published
publication: IEEE transactions on biomedical engineering
volume: 47
number: 5
pagerange: 589-593
refereed: TRUE
citation:   Vigário, Dr Ricardo and Särelä, Mr Jaakko and Jousmäki, Dr Veikko and Hämäläinen, Dr Matti and Oja, prof. Erkki  (2000) Independent component approach to the analysis of EEG and MEG recordings.  [Journal (Paginated)]     
document_url: http://cogprints.org/3639/1/TBME2000.pdf