Evaluation of window size in classification of epileptic short-term EEG signals using a Brain Computer Interface software


  • K. D. Tzimourta Medical Physics Laboratory, Medical School, University of Ioannina, Greece
  • L. G. Astrakas Medical Physics Laboratory, Medical School, University of Ioannina, Greece
  • A. M. Gianni Department of Computer Engineering, Technological Educational Institute of Epirus, Greece
  • A. T. Tzallas Department of Computer Engineering, Technological Educational Institute of Epirus, Greece
  • N. Giannakeas Department of Computer Engineering, Technological Educational Institute ofEpirus, Greece
  • I. Paliokas Information Technologies Institute, Centre for Research and Technology, Greece
  • D. G. Tsalikakis Department of Informatics and Telecommunications Engineering, University of Western Macedonia,Greece
  • M. G. Tsipouras Department of Informatics and Telecommunications Engineering, University of Western Macedonia, Greece
Volume: 8 | Issue: 4 | Pages: 3093-3097 | August 2018 | https://doi.org/10.48084/etasr.2031


The complexity of epilepsy created a fertile ground for further research in automated methods, attempting to help the epileptologists’ task. Over the past years, great breakthroughs have emerged in computer-aided analysis and the advent of Brain Computer Interface (BCI) systems has greatly facilitated the automated seizure analysis. In this study, an evaluation of the window size in automated seizure detection is proposed. The EEG signals from the University of Bonn was employed and segmented into 24 epochs of different window lengths with 50% overlap each time. Statistical and spectral features were extracted in the OpenViBE scenario that were used to train four different classifiers. Results in terms of accuracy were above 80% for the Decision Trees classifier. Also, results indicated that different window sizes provide small variations in classification accuracy.


Epilepsy, EEG, seizure detection, window size, Brain Computer Interface


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How to Cite

K. D. Tzimourta, “Evaluation of window size in classification of epileptic short-term EEG signals using a Brain Computer Interface software”, Eng. Technol. Appl. Sci. Res., vol. 8, no. 4, pp. 3093–3097, Aug. 2018.


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