期刊
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 47, 期 5, 页码 589-593出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/10.841330
关键词
independent component analysis (ICA); blind source separation (BSS); unsupervised learning; electroencephalography (EEG); magnetoencephalography (MEG); artifact removal; auditory evoked field (AEF); somatosensory evoked field (SEF)
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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