A comparative stationarity analysis of EEG signals

Date

2017-11-01

Author

RASOULZADEH, VESAL
Erkuş, Ekin Can
Yoğurt, Taha Alper
Ulusoy, İlkay
Zergeroglu, S. Aykan

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While developing models of brain functioning by using time series data, the stationary interval of the time series should be used to model the corresponding state of the brain. Here it is assumed that at the borders of stationarity, brain changes its state where a state is considered as a group of brain regions working together. If the whole nonstationary time series is used, many different brain states could be included in one model. However, it is very hard to decide the stationary intervals for such a complicated system as brain. There are some methods, which have proved their performances based on manually constructed synthetic data, in the literature. Only very few results with EEG data have been presented. But, there is usually no ground truth accompanying the data to make an evaluation. In this study, suitable approaches for stationary analysis were applied on visual evoked potentials (VEP) where we can approximately know the possible stationary intervals due to the properties of the experiment during which the data was recorded. Experts designed the experiment and marked the possible borders of the intervals carefully. Parameters of the methods were set automatically. We compared the manually marked intervals with the intervals detected automatically by the applied methods and evaluated the methods in terms of their performances of estimating the stationary intervals of EEG signals.

Subject Keywords

EEG, Stationary, VEP, Fractal, Hilbert-Huang transform, Statistical analysis

URI

https://hdl.handle.net/11511/30058

Journal

ANNALS OF OPERATIONS RESEARCH

DOI

https://doi.org/10.1007/s10479-016-2187-3

Collections

Graduate School of Natural and Applied Sciences, Article

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Citation Formats

V. RASOULZADEH, E. C. Erkuş, T. A. Yoğurt, İ. Ulusoy, and S. A. Zergeroglu, “A comparative stationarity analysis of EEG signals,” ANNALS OF OPERATIONS RESEARCH, pp. 133–157, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/30058.