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

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

244
views

0
downloads

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

Suggestions

OpenMETU
Core

A Computational approach to detect inhomogeneities in time series data Yazıcı, Ceyda; Yozgatlıgil, Ceylan; Batmaz, İnci; Department of Statistics (2017) Detection of possible inhomogeneity within a series is an important problem in time series data. There are many sources from which inhomogeneity can be originated such as mean shift, variance and trend change, gradual change, or sudden decrease or increase in time series. Since time series has many application areas, the detection of changepoints should be investigated before conducting any analysis. Available methods have certain drawbacks that may lead to unreliable inferences. These include the need of i...
A temporal neuro-fuzzy approach for time-series analysis Yılmaz (Şişman), Nuran Arzu; Alpaslan, Ferda Nur; Department of Computer Engineering (2003) The subject of this thesis is to develop a temporal neuro-fuzzy system for fore- casting the future behavior of a multivariate time series data. The system has two components combined by means of a system interface. First, a rule extraction method is designed which is named Fuzzy MAR (Multivari- ate Auto-regression). The method produces the temporal relationships between each of the variables and past values of all variables in the multivariate time series system in the form of fuzzy rules. These rules may ...
An Information Theoretic Approach to Classify Cognitive States Using fMRI Onal, Itir; Ozay, Mete; Firat, Orhan; GİLLAM, İLKE; Yarman Vural, Fatoş Tunay (2013-11-13) In this study, an information theoretic approach is proposed to model brain connectivity during a cognitive processing task, measured by functional Magnetic Resonance Imaging (fMRI). For this purpose, a local mesh of varying size is formed around each voxel. The arc weights of each mesh are estimated using a linear regression model by minimizing the squared error. Then, the optimal mesh size for each sample, that represents the information distribution in the brain, is estimated by minimizing various inform...
A temporal neural network model for constructing connectionist expert system knowledge bases Alpaslan, Ferda Nur (Elsevier BV, 1996-04-01) This paper introduces a temporal feedforward neural network model that can be applied to a number of neural network application areas, including connectionist expert systems. The neural network model has a multi-layer structure, i.e. the number of layers is not limited. Also, the model has the flexibility of defining output nodes in any layer. This is especially important for connectionist expert system applications.
An analysis of a linearly extrapolated BDF2 subgrid artificial viscosity method for incompressible flows Demir, Medine (Elsevier BV, 2020-10-01) This report extends the mathematical support of a subgrid artificial viscosity (SAV) method to simulate the incompressible Navier-Stokes equations to better performing a linearly extrapolated BDF2 (BDF2LE) time discretization. The method considers the viscous term as a combination of the vorticity and the grad-div stabilization term. SAV method introduces global stabilization by adding a term, then anti-diffuses through the extra mixed variables. We present a detailed analysis of conservation laws, includin...

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.