Independent component approach to the analysis of EEG and MEG recordings
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- 1 May 2000
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Biomedical Engineering
- Vol. 47 (5) , 589-593
- https://doi.org/10.1109/10.841330
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
This publication has 23 references indexed in Scilit:
- Independent component approach to the analysis of EEG and MEG recordingsIEEE Transactions on Biomedical Engineering, 2000
- Extraction of event-related signals from multichannel bioelectrical measurementsIEEE Transactions on Biomedical Engineering, 2000
- Artifact reduction in magnetoneurography based on time-delayed second-order correlationsIEEE Transactions on Biomedical Engineering, 2000
- Gaussian moments for noisy independent component analysisIEEE Signal Processing Letters, 1999
- Somatosensory evoked fields to large-area vibrotactile stimuliClinical Neurophysiology, 1999
- Independent Component Analysis in Wave Decomposition of Auditory Evoked FieldsPublished by Springer Nature ,1998
- A Fast Fixed-Point Algorithm for Independent Component AnalysisNeural Computation, 1997
- Independent component analysis, A new concept?Signal Processing, 1994
- Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brainReviews of Modern Physics, 1993
- Two bilateral sources of the late AEP as identified by a spatio-temporal dipole modelElectroencephalography and Clinical Neurophysiology/Evoked Potentials Section, 1985