• Mahrad Pouryosef, Roozbeh Abedini-Nassab, and AkramiSeyed Mohammad RezaSMRDivision of Mechatronics Engineering, Faculty of Mechanical Engineering, University of Tabriz, 29 Bahman Blvd, Tabriz 51666 14761, Iran..
• Division of Mechatronics Engineering, Faculty of Mechanical Engineering, University of Tabriz, 29 Bahman Blvd, Tabriz 51666 14761, Iran.
• Neuroscience. 2024 Mar 16; 541: 354935-49.

AbstractThe precise electroencephalogram (EEG) signal classification with the highest possible accuracy is a key goal in the brain-computer interface (BCI). Considering the complexity and nonstationary nature of the EEG signals, there is an urgent need for effective feature extraction and data mining techniques. Here, we introduce a novel pipeline based on Bat and genetic algorithms for feature construction and dimension reduction of EEG signals. After wavelet extraction and segmentation, the Bat algorithm identifies the most relevant features. We use these features and a genetic algorithm combined with a neural network method to automatically classify the segments of the epilepsy EEG signals. We also use available classification methods based on k-Nearest Neighbors or naïve Bayes for comparison purposes. The code distinguishes individual signals within various combinations of data obtained from healthy volunteers with open or closed eyes and patients suffering from epilepsy disorders during seizure-free periods or seizure activities. Compared to the previously introduced methods, our proposed framework demonstrates a superior balance of high accuracy and short runtime. The minimum achieved accuracies for balanced and unbalanced classes are 100% and 75.9%, respectively. This approach has the potential for direct applications in clinics, enabling accurate and rapid analysis of the epilepsy EEG signals obtained from patients.Copyright © 2024 IBRO. Published by Elsevier Inc. All rights reserved.

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