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.
NeuroImage