Abstract

SANCHEZ, Frank  and  HERNANDEZ, Alher Mauricio. Autoregressive modelling of electroencephalographs signals for medical simulators. Ing. Desarro. [online]. 2017, vol.35, n.2, pp.337-356. ISSN 0122-3461.

The recording of brain electrical activity has led to a greater understanding of different neurophysiological states, has even made possible the diagnosis of some neuronal disorders, hence the importance of characterization and understanding of the different morphologies that may have electroencephalography signals (EEG). The mathematical modeling of biomedical signals facilitates the development of simulators that can be useful as medical training tools on computers or mobile devices. This paper presents the autoregressive (AR) modeling and simulation of EEG signals in different physiological states: seizures, resting with eyes open and eyes closed, and also under the presence of some of the most common artifacts: muscle, eye blinking, electrode "pop", and 60-Hz. The performance of the models has been validated in the time domain using the percentage of fitting (FIT), which was always above 70%, and in the frequency domain through energy of the characteristic frequency bands of the EEG. The modeling methodology, figures of simulated signals and the values of the parameters evaluated are presented. The wide variety of EEG signals modeled allow the development of brain signals simulators for training of medical personnel, and also for the analysis and characterization of EEG signals.

Keywords : Autoregressive processes; electroencephalography; medical simulators; signal modeling.

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