Metaheuristic Optimization Review MOR 3066-280X 10.54216/MOR https://www.americaspg.com/journals/show/4204 2024 2024 Higher Technological Institute for Applied Health Sciences, Department of Medical Equipment Maintenance, Dakahlia, Egypt; Biomedical Engineering Department, Faculty of Engineering, Mansoura University, Egypt EL EL-Sayed Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University, Egypt; Dean of Faculty of Artificial Intelligence and information, Horus University, Egypt Hossam El-Din Moustafa Department of Neurology, Mansoura Faculty of Medicine, Mansoura, Egypt W. Mustafa Department of Electrical Engineering, Faculty of Engineering, Mansoura University, Egypt Islam Ismael Delta Higher Institute of Engineering and Technology Department for Communications and Electronics Mansoura 35511, Egypt; Applied Science Research Center. Applied Science Private University, Amman, Jordan EL-Sayed M.El M.El-Kenawy Brain-computer interface (BCI) systems based on electroencephalography (EEG) are applications that allow human-to-machine communication with intuitive (near-transparent) control, whose neural commands are decoded based on intentional movement. Recent research on the topic of machine learning (ML) has been able to greatly enhance the classification of the EEG-signals associated with the movement of the hands, head movements, and mobility movements of the eyes. The developments allow various utilization across assistive technologies, prosthetic control, and non-verbal communication. EEG, however, is highly non-stationery and noise-sensitive, so advanced preprocessing and optimization methods have to be applied to optimize performance in classification. This paper outlines an in-depth review of some of the most popular ML algorithms, i.e. support vector machines (SVMs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), and optimization methods, i.e., genetic algorithms (GAs), particle swarm optimization (PSO), and transfer learning. We point out existing problems in the processing of EEG signals and suggest directions in the future that will improve the robustness, generalization, and real-time behavior of BCI. 2026 2026 01 21 10.54216/MOR.050201 https://www.americaspg.com/articleinfo/41/show/4204