TY  - GEN
ID  - cogprints3639
UR  - http://cogprints.org/3639/
A1  - Vigário, Dr Ricardo
A1  - Särelä, Mr Jaakko
A1  - Jousmäki, Dr Veikko
A1  - Hämäläinen, Dr Matti
A1  - Oja, prof. Erkki
Y1  - 2000///
N2  - 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.

KW  - independent component analysis (ICA)
KW  -  blind source    separation (BSS)
KW  -  unsupervised learning
KW  -  electroencephalography (EEG)
KW  -  magnetoencephalography(MEG)
KW  -  artifact removal
KW  -  auditory evoked field (AEF)
KW  -  somatosensory evoked field (SEF)

TI  - Independent component approach to the analysis of EEG and MEG recordings
SP  - 589
AV  - public
EP  - 593
ER  -

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