Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis |
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| Authors: | Aapo Hyv rinen, Pavan Ramkumar, Lauri Parkkonen,Riitta Hari |
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| Affiliation: | aDepartment of Mathematics and Statistics, Department of Computer Science, HIIT, and Department of Psychology, University of Helsinki, Finland;bBrain Research Unit, LTL, Helsinki University of Technology, Finland;cAdvanced Magnetic Imaging Centre, Helsinki University of Technology, Finland |
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| Abstract: | 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. |
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| Keywords: | Magnetoencephalography (MEG) Independent component analysis Brain rhythms Resting state |
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