Effect of Changing CNN Classifier Parameters on EEG Signals Recognition Ratio

Authors

  • Tarek Ali Tishreen university
  • Kinda Abo Kassem Tishreen University
  • Oulfat Jolaha Tishreen University

Keywords:

Deep Learning, Convolutional Neural Nets (CNN), Brain Computer Interface (BCI), Electroencephalography (EEG), Common Spatial Pattern (CSP).

Abstract

Brain Computer Interface (BCI), especially systems for recognizing brain signals using deep learning after characterizing these signals as EEG (Electroencephalography), is one of the important research topics that arouse the interest of many researchers currently. Convolutional Neural Nets (CNN) is one of the most important deep learning classifiers used in this recognition process, but the parameters of this classifier have not yet been precisely defined so that it gives the highest recognition rate and the lowest possible training and recognition time.

This research proposes a system for recognizing EEG signals using the CNN network, while studying the effect of changing the parameters of this network on the recognition rate, training time, and recognition time of brain signals, as a result the proposed recognition system was achieved 76.38 % recognition rate, And the reduction of classifier training time (3 seconds) by using Common Spatial Pattern (CSP) in the preprocessing of IV2b dataset, and a recognition rate of 76.533% was reached by adding a layer to the proposed classifier.

Downloads

Published

2023-09-07

How to Cite

1.
علي ط, كندة أبو قاسم, ألفت جولحة. Effect of Changing CNN Classifier Parameters on EEG Signals Recognition Ratio . Tuj-eng [Internet]. 2023Sep.7 [cited 2024Nov.24];45(4):95-107. Available from: https://journal.tishreen.edu.sy/index.php/engscnc/article/view/14462

Issue

Vol. 45 No. 4 (2023): Tishreen University Journal of Research and Scientific Studies - Engineering Sciences Series

Section

Articles

License

Copyright (c) 1970 Tttps://creativecommons.org/licenses/by-nc-sa/4.0/

Creative Commons License

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

The authors retain the copyright and grant the right to publish in the magazine for the first time with the transfer of the commercial right to Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series

 Under a CC BY- NC-SA 04 license that allows others to share the work with of the work's authorship and initial publication in this journal. Authors can use a copy of their articles in their scientific activity, and on their scientific websites, provided that the place of publication is indicted in  Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series   .  The Readers have the right to send, print and subscribe to the initial version of the article, and the title of  Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series     Publisher

journal uses a CC BY-NC-SA license which mean

You are free to:

  • Adapt — remix, transform, and build upon the material
  •  
  • The licensor cannot revoke these freedoms as long as you follow the license terms.

 

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • NonCommercial — You may not use the material for commercial purposes.
  • ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
  • No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.