Stochastic Oscillator Model of EEG Based on Information Content and Complexity

In this study, a stochastic Duffing - van der Pol coupled two oscillator system is designed to produce output matching the information content, complexity measure, and frequency content of actual electroencephalography (EEG) signals. This is achieved by deriving the oscillator model parameters and noise intensity using an optimization scheme whose objective is to minimize a weighed average of errors in sample entropy, Shannon entropy, and powers of the major brain frequency bands. The signals produced by the optimal model are then compared with the EEG signal using phase portrait reconstruction. The study shows that the model can effectively reproduce signals that match EEG recorded under different brain states with respect to multiple metrics.