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Modeling from Time Series of Complex Brain Signals

Galina S. Panayotova1 and Dimitar A. Dimitrov2
1.Department Computer Science, University of Library Studies and Informational Technologies, Sofia, Bulgaria
2.University “Prof. Dr. As. Zlatarov”- Burgas, Bulgaria
Abstract—Signals obtained from most of real-world systems, especially from living organisms, are irregular, often chaotic, non-stationary, and noise-corrupted. Since modern measuring devices usually realize digital processing of information, recordings of the signals take the form of a discrete sequence of samples (a time series). In the paper given a brief overview of the possibilities of such experimental data processing based on reconstruction and usage of a predictive empirical model of a time series. Brain signals can be recorded by brainwave controlled applications, such as EMotiv Epoc +14. The paper investigates the models of the observed brain signals using time series, analyzes their applicability and develops new statistical models for their study. 

Index Terms—time series, signal processing, reconstruction of signals, empirical modelling

Cite: Galina S. Panayotova and Dimitar A. Dimitrov, "Modeling from Time Series of Complex Brain Signals," International Journal of Signal Processing Systems, Vol. 9, No. 1, pp. 1-6, March 2021. doi: 10.18178/ijsps.9.1.1-6

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