title: Independent component approach to the analysis of EEG and MEG recordings.
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 magnetoencephalographic (MEG) recordings. In addition, ICA has been applied to the analysis of brain signals evoked by sensory stimuli. This paper reviews our recent results in this field.
authors.affiliation: Laboratory of Computer and Information Science, Helsinki University of Technology, HUT, Finland. Ricardo.Vigario@hut.fi
authors.forename: R
authors.initials: R
authors.lastname: Vigário
authors.forename: J
authors.initials: J
authors.lastname: Särelä
authors.forename: V
authors.initials: V
authors.lastname: Jousmäki
authors.forename: M
authors.initials: M
authors.lastname: Hämäläinen
authors.forename: E
authors.initials: E
authors.lastname: Oja
mesh_terms: D000465:Algorithms; D016477:Artifacts; D004569:Electroencephalography; D005072:Evoked Potentials, Auditory / Q000502:physiology; D005073:Evoked Potentials, Somatosensory / Q000502:physiology; D006801:Humans; D015225:Magnetoencephalography; D012815:Signal Processing, Computer-Assisted