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 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.
Hyvärinen
Aapo
A
Department of Mathematics and Statistics, Department of Computer Science, HIIT, University of Helsinki, Finland. Aapo.Hyvarinen@helsinki.fi
Ramkumar
Pavan
P
Parkkonen
Lauri
L
Hari
Riitta
R
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology; D004569:Electroencephalography / Q000706:statistics & numerical data*; D005583:Fourier Analysis; D006801:Humans; D015225:Magnetoencephalography / Q000706:statistics & numerical data*; D015233:Models, Statistical; D016011:Normal Distribution; D025341:Principal Component Analysis; D015203:Reproducibility of Results