2836-7863ISSN (Online)
Volume 3 , Issue 2 , PP: 32-39, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Khaled Moaz 1 *
Doi: https://doi.org/10.54216/NIF.030205
Brain Computer Interface (BCI), especially systems for recognizing brain signals using 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.
CNN , EEG , Signals , Recognition ratio
[1] C. GUAN, S. LEE. Subject-Independent Brain-Computer Interfaces Based on Deep Convolutional Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 10, pp. 3839-3852, 2019.
[2] J. CHO, J. JEONG. O. KIM, S. LEE. A Novel Approach to Classify Natural Grasp Actions by Estimating Muscle Activity Patterns from EEG Signals. ArXiv: 00556v1, 2020.
[3] M. MIAO, W. HU, H. YIN, K. ZHANG. Spatial-Frequency Feature Learning and Classification of Motor Imagery EEG Based on Deep Convolution Neural Network. Computational and mathematical methods in medicine, Vol. 1981728. 2020.
[4] Y. SONG, L. YANG, L. XIE, L. JIA. Song, Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface. ArXiv: 2102.04456, 2021.
[5] K. GHANBAR, T.REZAII, A. FARZAMNIA, I. SAAD. Correlation-based common spatial pattern (CCSP): A novel extension of CSP for classification of motor imagery signal. PLoS ONE, Vol.16, NO. 3, 2021.
[6] G. XIAO, M. YE, B. XU, Z. CHEN, Q. REN. 4D Attention-based Neural Network for EEG Emotion Recognition. ArXiv: 2101.05484, 2021.
[7] R. LEEB, F. LEE, C. KEINRATH, R. SCHERER, H. BISCHOF, G. PFURTSCHELLER. Brain-computer communication: motivation, aim, and impact of exploring a virtual apartment. IEEE Transactions on Neural Systems and Rehabilitation Engineering 15, 473–482, 2007.
[8] ZOLTAN J. KOLES, MICHAEL S. LAZARET, STEVEN Z. ZHOU. Spatial patterns underlying population differences in the background EEG. Brain topography, Vol. 2 (4) pp. 275-284, 1990.
[9] H. MEISHERI, N. RAMRAO, S. MITRA, Multiclass Common Spatial Pattern for EEG based Brain Computer Interface with Adaptive Learning Classifier. arXiv: abs/1802.09046, 2018.
[10] M. CONGEDO, L. KORCZOWSKI, A. DELORME AND F. LOPES DA SILVA. Spatio-temporal common pattern: A companion method for ERP analysis in the time domain. Journal of Neuroscience Methods, Vol. 267, pp. 74-88, 2016.
[11] C. SWEENEY, E. ENNIS, M. MULVENNA, R. BOND, S. O'NEILL. How Machine Learning Classification Accuracy Changes in a Happiness Dataset with Different Demographic Groups. Computers, VOL.11, NO.5, 2022.
[12] O. Trifonova, P. Lokhov, metabolic Profiling of Human Blood. Biomeditsinskaya Khimiya, Vol. 60, No. 3.pp. 281-294, 2014.
