PCA and ICA for the extraction of EEG components in cerebral death assessment

The electroencephalogram (EEG) analysis provides a functional tool to verify a qualitative clinical check. In this paper some techniques, i.e. principal component analysis (PCA) and independent component analysis (ICA), are implemented in order to extrapolate in EEG signals very few dominant components that contain almost all the information necessary to have an adequate knowledge of the brain activity. To obtain that, the compression ability of PCA is mixed with the statistical independence property of the ICA. The achieved results show that in most cases of cerebral death diagnosis, in which the EEG analysis is performed when the brain activity is very reduced, even few components are enough to depict the complete brain activity.