Prediction of Synchrostate Transitions in EEG Signals Using Markov Chain Models

Although EEG signals due to their high temporal resolution show highly stochastic temporal evolution, it has been found that the scalp potential topographies are not so random and follow finite sets of small number of quasi-stable patterns which are termed as microstates [2]. Recently, Jamal et al. [3] investigated the temporal evolution of the frequency band-specific phase difference topographies to find periods of phase locking in multichannel EEG signals. It has been found in [4] that the phase difference topographies do not change abruptly and microstate-like quasi-stable phase locked patterns are observed in a temporal resolution of the order of milliseconds. These small number of stable phase synchronized patterns are termed as synchrostates, which switches from one to the other within the time interval of a cognitive task. The existence of synchrostates during face perception tasks was first observed in the beta (β) band (13-30 Hz) with different ensembles of EEG signals [4]. For similar visual stimuli, the interstate switching patterns only slightly change among different ensembles or trials [4], however it is different for different stimuli and also across different groups of people [3].