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 -