title: Markov process amplitude EEG model for spontaneous background activity.
abstract: The Markov process amplitude (MPA) EEG model effectively representing spontaneous brain activity of the EEG was introduced. The relationship between the electrical mechanism for EEG generation and the proposed model was also investigated. The MPA EEG model was described by the sinusoidal waves with the randomly fluctuating amplitude of the first-order Markov process. The parameters of the MPA EEG model were determined optimally based on the real EEG records. The results of model outputs in the frequency domain demonstrated an excellent fit with the power spectrum of the corresponding EEG. The simulated model signal in the time domain also showed good agreement with the EEG time series. The satisfactory results from the MPA EEG model suggest its possible applicability in clinical practice. Furthermore, from the high goodness of fit, the authors think that the neurons oscillate at fixed frequencies and are modulated by synaptic interactions in accordance with the first-order Markov process.
authors.affiliation: Department of Advanced Systems Control Engineering, Saga University, Japan.
authors.forename: O
authors.initials: O
authors.lastname: Bai
authors.forename: M
authors.initials: M
authors.lastname: Nakamura
authors.forename: S
authors.initials: S
authors.lastname: Nishida
authors.forename: A
authors.initials: A
authors.lastname: Ikeda And
authors.forename: H
authors.initials: H
authors.lastname: Shibasaki
mesh_terms: D000328:Adult; D001931:Brain Mapping; D002540:Cerebral Cortex / Q000503:physiopathology*; D004569:Electroencephalography / Q000706:statistics & numerical data*; D004827:Epilepsy / Q000175:diagnosis / Q000503:physiopathology*; D005071:Evoked Potentials / Q000502:physiology; D005583:Fourier Analysis; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D012815:Signal Processing, Computer-Assisted