Independent component approach to the analysis of EEG and MEG recordings

2000/05/01 by Ricardo Vigário, R. Vigario, Jaakko Särelä +6 · 815 citations
Computer Science · Neuroscience · Psychology · #Artifact (error) #Artificial intelligence #Blind Source Separation Techniques #Blind signal separation #Computer science #Digital signal processing #EEG and Brain-Computer Interfaces #Electroencephalography #Feature extraction #Independent component analysis #Magnetoencephalography #Neural dynamics and brain function #Neurophysiology #Neuroscience #Pattern recognition (psychology) #Psychology #Representation (politics) #Signal processing #Speech recognition

paper · doi:10.1109/10.841330

published in IEEE Transactions on Biomedical Engineering 47(5), 589-593 (Institute of Electrical and Electronics Engineers)

openalex publication_date 2000/05/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/26

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.

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