?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Independent+component+approach+to+the+analysis+of+EEG+and+MEG+recordings&rft.creator=Vig%C3%A1rio%2C+Dr+Ricardo&rft.creator=S%C3%A4rel%C3%A4%2C+Mr+Jaakko&rft.creator=Jousm%C3%A4ki%2C+Dr+Veikko&rft.creator=H%C3%A4m%C3%A4l%C3%A4inen%2C+Dr+Matti&rft.creator=Oja%2C+prof.+Erkki&rft.subject=Brain+Imaging&rft.subject=Statistical+Models&rft.subject=Machine+Learning&rft.description=Multichannel+recordings+of+the+electromagnetic+fields%0Aemerging+from+neural+currents+in+the+brain+generate+large+amounts%0Aof+data.+Suitable+feature+extraction+methods+are%2C+therefore%2C+useful%0Ato+facilitate+the+representation+and+interpretation+of+the+data.%0ARecently+developed+independent+component+analysis+(ICA)+has%0Abeen+shown+to+be+an+efficient+tool+for+artifact+identification+and%0Aextraction+from+electroencephalographic+(EEG)+and+magnetoen-%0Acephalographic+(MEG)+recordings.+In+addition%2C+ICA+has+been+ap-%0Aplied+to+the+analysis+of+brain+signals+evoked+by+sensory+stimuli.+This%0Apaper+reviews+our+recent+results+in+this+field.%0A&rft.date=2000&rft.type=Journal+(Paginated)&rft.type=PeerReviewed&rft.format=application%2Fpdf&rft.identifier=http%3A%2F%2Fcogprints.org%2F3639%2F1%2FTBME2000.pdf&rft.identifier=++Vig%C3%A1rio%2C+Dr+Ricardo+and+S%C3%A4rel%C3%A4%2C+Mr+Jaakko+and+Jousm%C3%A4ki%2C+Dr+Veikko+and+H%C3%A4m%C3%A4l%C3%A4inen%2C+Dr+Matti+and+Oja%2C+prof.+Erkki++(2000)+Independent+component+approach+to+the+analysis+of+EEG+and+MEG+recordings.++%5BJournal+(Paginated)%5D+++++&rft.relation=http%3A%2F%2Fcogprints.org%2F3639%2F