A collaborative filtering approach for quasi-brain-death EEG analysis

A novel method to evaluate the statistical significance differences between the groups of coma and brain death patients is presented. This is achieved based on the electroencephalogram (EEG) and by using a collaborative filtering structure with the least mean square (LMS) and least mean phase (LMP) adaptive filters. By virtue of a complex-valued representation of pair-wise EEG signals, the evolution of the mixing parameter is used as an indicator of the fundamental amplitude-phase relationships of EEG recordings. Simulations illustrate the suitability of this approach to differentiate between the coma and quasi-brain-death states.