Independent component analysis and beyond in brain imaging: EEG, MEG, fMRI, and PET

There is an increasing interest in analyzing brain images from various imaging modalities, that record the brain activity during functional task, for understanding how the brain functions as well as for the diagnosis and treatment of brain disease. Independent component analysis (ICA), an exploratory and unsupervised technique, separates various signal sources mixed in brain imaging signals such as brain activation and noise, assuming that the sources are mutually independent in the complete statistical sense. This paper summarizes various applications of ICA in processing brain imaging signals: EEG, MEG, fMRI or PET. We highlight the current issues and limitations of applying ICA in these applications, current, and future directions of research.