Classification of brain states using principal components analysis of cortical EEG synchronization and HMM

The state of brain and its rapid transition from one state to the other is responsible for various activities and cognitive functions. These brain states are the result of balanced coordination between integrating and segregating activities of different lobes through rhythmic oscillations. Such coordination has been studied in recent times through synchronization of EEG signals generated from different lobes. In this paper, the authors have considered Synchronization Likelihood (SL) to measure the synchronization or integration between the lobes. The synchronization information is stored in SL matrix and the principal components of an SL matrix have been used to represent the state of brain at any instant. Finally, the time series of weight vectors corresponding to the principal components of SL matrices at each time point has been used to classify different states of brain at different stages of a sleep deprived experiment.