E3S Web Conf.
Volume 351, 202210th International Conference on Innovation, Modern Applied Science & Environmental Studies (ICIES’2022)
|Number of page(s)||5|
|Published online||24 May 2022|
An enhanced EEG prediction system for motor cortex-imagery tasks using SVM
Faculty of Science and Technology, Hassan II University, Casablanca, Morocco
* Corresponding author: email@example.com
After the emergence of many new technologies, it is possible to search on the development of new devices that can be predicting what is happening in human thought based on EEG signals, such as the method used this paper contains a novel classification of the EEG signals acquired for multiple motor cortex-imagery tasks, where this method was based on the use of the Extra Tree algorithm to well select the best channels that were used for the acquisition of EEG signals, then the use of support vector machine (SVM) algorithm for data classification, moreover this work uses grey wolf optimizer (GWO) algorithm to improve all SVM parameters quickly and to converge the accuracy of the system towards the highest possible values. As a result, this work shows that the accuracy of prediction of motor cortex-imagery based EEG signals can be increased more than 99%. Also, this paper contains a comparison with other methods of the literature.
Key words: Brain-computer interface (BCI) / Electroencephalogram (EEG) / Data analysis / Machine learning / Parameters optimization
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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