Mohand Lokman Al DabagNalan ǑzkurtShaima Miqdad Mohamed NajeebT. Yildirim , B.M. Ozyildirim2025-10-062018978153867786510.1109/ASYU.2018.8554029https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059972164&doi=10.1109%2FASYU.2018.8554029&partnerID=40&md5=d7dc2fe3bdc5d1cafbb3bdfd326e7a99https://gcris.yasar.edu.tr/handle/123456789/9509Electroencephalography (EEG) classification for mental tasks is the crucial part of the braincomputer interface. Many studies try to extract discriminative features from EEG signals. In this study feature selection algorithm based on genetic algorithm (GA) was implemented to find the best features that describe EEG signal. The best features are searched among ten statistical features calculated from the cross-correlation of effective channel with relevant EEG channels in the proposed study. A comparison was made after and before feature selection in two major viewpoints: classification accuracy and computation time. Multi-Layer Perceptron Neural Network (MLP) and Support Vector Machine (SVM) are used to classify left and right finger movements of 13 subjects. The overall classification performance is enhanced about 1% for both classifiers after feature selection. Computation time has reduced about 34% in SVM classifier and there is huge reduction about 84 % in MLP. © 2019 Elsevier B.V. All rights reserved.EnglishBrain Computer Interface Bci, Electroencephalogram Eeg, Genetic Algorithm, Neural Networks, Real/imaginary Movement Classification, Support Vector Machine Svm, Biomedical Signal Processing, Brain Computer Interface, Classification (of Information), Data Mining, Electrophysiology, Feature Extraction, Genetic Algorithms, Intelligent Systems, Network Layers, Neural Networks, Support Vector Machines, Classification Accuracy, Classification Performance, Cross Correlations, Discriminative Features, Feature Selection Algorithm, Feature Selection And Classification, Multi-layer Perceptron Neural Networks, Statistical Features, ElectroencephalographyBiomedical signal processing, Brain computer interface, Classification (of information), Data mining, Electrophysiology, Feature extraction, Genetic algorithms, Intelligent systems, Network layers, Neural networks, Support vector machines, Classification accuracy, Classification performance, Cross correlations, Discriminative features, Feature selection algorithm, Feature selection and classification, Multi-layer perceptron neural networks, Statistical features, ElectroencephalographyConference Object