@misc{cogprints3639,
          volume = {47},
          number = {5},
           title = {Independent component approach to the analysis of EEG and MEG recordings},
          author = {Dr Ricardo Vig{\'a}rio and Mr Jaakko S{\"a}rel{\"a} and Dr Veikko Jousm{\"a}ki and Dr Matti H{\"a}m{\"a}l{\"a}inen and prof. Erkki Oja},
            year = {2000},
           pages = {589--593},
         journal = {IEEE transactions on biomedical engineering},
        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)
},
             url = {http://cogprints.org/3639/},
        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.
}
}

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