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

Neuroimage. 2010 Jan 1;49(1):257-71. doi: 10.1016/j.neuroimage.2009.08.028. Epub 2009 Aug 20.


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.

MeSH terms

  • Algorithms
  • Brain / anatomy & histology
  • Electroencephalography / statistics & numerical data*
  • Fourier Analysis
  • Humans
  • Magnetoencephalography / statistics & numerical data*
  • Models, Statistical
  • Normal Distribution
  • Principal Component Analysis
  • Reproducibility of Results