Prediction of Synchrostate Transitions in EEG Signals Using Markov Chain Models
This work addresses the challenge of modeling dynamic brain states in EEG analysis, which is incremental as it applies an existing stochastic method to a specific neuroimaging context.
The paper tackled the problem of predicting transitions between quasi-stable phase synchronized patterns (synchrostates) in EEG signals by using Markov chain models, achieving prediction accuracies evaluated through cross-validation on 100 trials of 128-channel EEG data during face perception tasks.
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.