aspg-39-3064-1792222459 20261017073419 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Neutrosophic and Information Fusion NIF 2836-7863 2024 3 2 Khaled Moaz University of Mosul, department of computer science and mathematics, Mosul, Iraq Brain Computer Interface (BCI), especially systems for recognizing brain signals using EEG (Electroencephalogra-phy), 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 achieved a 76.38% recognition rate and reduced classifier training time by 3 seconds by using Common Spatial Pattern (CSP) in preprocessing the IV2b dataset. A recognition rate of 76.533% was reached by adding a layer to the proposed classifier. 2024 22 27 3064 https://creativecommons.org/licenses/by/4.0/ 10.54216/NIF.030205 https://americaspg.com/journal/39/article/3064 https://americaspg.com/storage/articles/manuscripts/01M3W31V3C7ZGNKKTHT15ZF9N9.pdf