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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Shaima Mohammed Ali
Northen Technical University
Computer Science & IT

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Finding the discriminative frequencies of motor electroencephalography signal using genetic algorithm Shaima Miqdad Mohamed Najeeb; Haider Th. Salim Al Rikabi; Shaima Mohammed Ali
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 1: February 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i1.17884

Abstract

A crucial part of the brain-computer interface is a classification of electroencephalography (EEG) motor tasks. Artifacts such as eye and muscle movements corrupt EEG signal and reduce the classification performance. Many studies try to extract not redundant and discriminative features from EEG signals. Therefore, this study proposed a signal preprocessing and feature extraction method for EEG classification. It consists of removing the artifacts by using discrete fourier transform (DFT) as an ideal filter for specific frequencies. It also cross-correlates the EEG channels with the effective channels to emphases the EEG motor signals. Then the resultant from cross correlation are statistical calculated to extract feature for classifying a left and right finger movements using support vector machine (SVM). The genetic algorithm was applied to find the discriminative frequencies of DFT for the two EEG classes signal. The performance of the proposed method was determined by finger movement classification of 13 subjects and the experiments show that the average accuracy is above 93 percent.
Co-Authors Haider Th. Salim Al Rikabi Shaima Miqdad Mohamed Najeeb