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Independent component approach to the analysis of EEG and MEG recordings

IEEE Transactions on Biomedical Engineering · 2000 · Vol. 47(5) · pp. 589–593

Ricardo Vigário✉(Helsinki Institute for Information Technology)Jaakko Särelä(Helsinki Institute for Information Technology)V. JousmikiMatti Hämäläinen(University of Helsinki)Erkki Oja(Helsinki Institute for Information Technology)

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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.

Blind Source Separation Techniques Neural dynamics and brain function EEG and Brain-Computer InterfacesIndependent component analysisMagnetoencephalographyElectroencephalographyComputer scienceFeature extractionPattern recognition (psychology)Artifact (error)Artificial intelligenceSignal processingSpeech recognition

MeSH terms

AlgorithmsElectroencephalographyEvoked Potentials, AuditoryEvoked Potentials, SomatosensoryHumansSignal Processing, Computer-AssistedMagnetoencephalographyArtifacts

Citations

801

FWCI

17.16

field-weighted impact

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100

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99%

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