Prediction of Synchrostate Transitions in EEG Signals Using Markov Chain Models
IEEE Signal Processing Letters
Institute of Electrical and Electronics Engineers (IEEE)
© 2014 IEEE
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.
The work presented in this paper was supported by FP7 EU funded MICHELANGELO project, Grant Agreement #288241.
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.
Vol. 22 (2), pp. 149 - 152