Modeling from Time Series of Complex Brain Signals
Galina S. Panayotova, Dimitar A. Dimitrov · International Journal of Signal Processing Systems · 2021
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.