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A comparative stationarity analysis of EEG signals

  • S.I.: OR in Neuroscience
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Abstract

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.

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Authors and Affiliations

  1. Biomedical Engineering, METU, Ankara, Turkey

    V. Rasoulzadeh, E. C. Erkus & I. Ulusoy

  2. Department of Electrical and Electronics Engineering, METU, Ankara, Turkey

    T. A. Yogurt & I. Ulusoy

  3. Department of Interdisciplinary Neuroscience, Ankara University, Ankara, Turkey

    S. Aykan Zergeroğlu

Authors
  1. V. Rasoulzadeh
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  2. E. C. Erkus
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  3. T. A. Yogurt
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  4. I. Ulusoy
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  5. S. Aykan Zergeroğlu
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Correspondence to I. Ulusoy.

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Rasoulzadeh, V., Erkus, E.C., Yogurt, T.A. et al. A comparative stationarity analysis of EEG signals. Ann Oper Res 258, 133–157 (2017). https://doi.org/10.1007/s10479-016-2187-3

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