Prediction of Synchrostate Transitions in EEG Signals Using Markov Chain Models

Abstract

This paper proposes a stochastic model using the concept of Markov chains for the inter-state transitions of the millisecond order quasi-stable phase synchronized patterns or synchrostates, found in multi-channel Electroencephalogram (EEG) signals. First and second order transition probability matrices are estimated for Markov chain modelling from 100 trials of 128-channel EEG signals during two different face perception tasks. Prediction accuracies with such finite Markov chain models for synchrostate transition are also compared, under a data-partitioning based cross-validation scheme.

5 pages, 5 figures

References in corpus (3)

• Classification of Autism Spectrum Disorder Using Supervised Learning of Brain Connectivity Measures Extracted from Synchrostates
• Existence of Millisecond-order Stable States in Time-Varying Phase Synchronization Measure in EEG Signals
• Using Brain Connectivity Measure of EEG Synchrostates for Discriminating Typical and Autism Spectrum Disorder