Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis

Analysis of spontaneous EEG/MEG needs unsupervised learning methods. While independent component analysis (ICA) has been successfully applied on spontaneous fMRI, it seems to be too sensitive to technical artifacts in EEG/MEG. We propose to apply ICA on short-time Fourier transforms of EEG/MEG signa...

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Bibliographic Details
Published in:NeuroImage (Orlando, Fla.) Fla.), 2010-01, Vol.49 (1), p.257-271
Main Authors: Hyvärinen, Aapo, Ramkumar, Pavan, Parkkonen, Lauri, Hari, Riitta
Format: Article
Language:English
Subjects:
Algorithms
Brain - anatomy & histology
Brain rhythms
Electroencephalography
Electroencephalography - statistics & numerical data
Fourier Analysis
Fourier transforms
Humans
Independent component analysis
Magnetoencephalography (MEG)
Magnetoencephalography - statistics & numerical data
Models, Statistical
Normal Distribution
Principal Component Analysis
Reproducibility of Results
Resting state
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Summary:Analysis of spontaneous EEG/MEG needs unsupervised learning methods. While independent component analysis (ICA) has been successfully applied on spontaneous fMRI, it seems to be too sensitive to technical artifacts in EEG/MEG. We propose to apply ICA on short-time Fourier transforms of EEG/MEG signals, in order to find more “interesting” sources than with time-domain ICA, and to more meaningfully sort the obtained components. The method is especially useful for finding sources of rhythmic activity. Furthermore, we propose to use a complex mixing matrix to model sources which are spatially extended and have different phases in different EEG/MEG channels. Simulations with artificial data and experiments on resting-state MEG demonstrate the utility of the method.
ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2009.08.028