Complex Spectral Domain ICA, a New Dimension in Biosignal Analysis
Inventors: Jörn Anemüller and Scott Makeig
Potential Uses: EEG and Magnetic EEG signal analysis, Signal Separation, ICA
An improved complex signal separation method capable of opening a new window into the dynamics of EEG activity.
Independent component analysis (ICA) is effective in analyzing brain signals and in particular electroencephalographic (EEG) data. However, ICA algorithms presently applied to brain data rely on several idealized assumptions about the underlying processes that may not be fully applicable. Presently ICA analysis of brain data is carried out assuming a linear and instantaneous mixing process that can be expressed mathematically as multiplication by a single mixing matrix. In the standard ICA model, component signal sources are viewed as neural activity occurring in a perfectly synchronized manner within spatially fixed cortical domains. This does not take into account the possible spatio-temporal dynamics underlying neural processes. One way to exhibit more complex dynamics is to assume a convoluting mixing model. Also neglected in the standard ICA model, is the spectral quality of EEG signals. EEG activity has distinctive characteristics in the different frequency bands which may be associated with different physiological processes. These shortcomings can be overcome and EEG signal analysis can be enhanced using the new method of analysis of brain data. The invention is based on spectral decomposition of the sensor signals and subsequent analysis within distinct spectral bands by means of a complex algorithm for independent component analysis. Other recording techniques, such as magnetoencephalogram,(MEG) or functional magnetic resonance imaging (fMRI), and other electrical recordings from the human body such as electromyographic (EMG) and electrocardiographic (ECG) recording can also benefit from the new method.