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.

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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 2024Dec.26];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

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