Prediction of Synchrostate Transitions in EEG Signals Using Markov Chain Models
Jamal, W; Das, S; Oprescu, I-A; et al.Maharatna, K
Date: 4 September 2014
Journal
IEEE Signal Processing Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publisher DOI
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 ...
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.
Mathematics and Statistics
Faculty of Environment, Science and Economy
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