Independent component approach to the analysis of EEG and MEG recordings

Author

Vigário, Ricardo ; Särelä, Jaakko ; Jousmiki, V. ; Hämäläinen, Matti ; Oja, Erkki

Author_Institution

Lab. of Comput. & Inf. Sci., Helsinki Univ. of Technol., Espoo, Finland

Volume

47

Issue

5

fYear

2000

fDate

5/1/2000 12:00:00 AM

Firstpage

589

Lastpage

593

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.

Keywords

auditory evoked potentials; electroencephalography; feature extraction; magnetoencephalography; medical signal processing; statistical analysis; EEG recordings; MEG recordings; artifact identification; averaged auditory evoked fields; brain; brain signals; electroencephalographic recording; electromagnetic fields; feature extraction; independent component approach; magnetoencephalographic recording; multichannel recordings; neural currents; sensory stimuli; Blind source separation; Data mining; Electroencephalography; Feature extraction; Independent component analysis; Magnetic analysis; Magnetic separation; Signal analysis; Signal processing algorithms; Source separation; Algorithms; Artifacts; Electroencephalography; Evoked Potentials, Auditory; Evoked Potentials, Somatosensory; Humans; Magnetoencephalography; Signal Processing, Computer-Assisted;

fLanguage

English

Journal_Title

Biomedical Engineering, IEEE Transactions on

Publisher

ieee

ISSN

0018-9294

Type

jour

DOI

10.1109/10.841330

Filename

841330