TELKOMNIKA Telecommunication, Computing, Electronics and Control
Finding the discriminative frequencies of motor electroencephalography signal using genetic algorithm

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Finding the discriminative frequencies of motor electroencephalography signal using genetic algorithm

Brain computer interface
Discrete fourier transform
Electroencephalogram
Genetic algorithm
Support vector machine

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.

Shaima Miqdad Mohamed Najeeb, Haider Th. Salim Al Rikabi, Shaima Mohammed Ali

http://journal.uad.ac.id/index.php/TELKOMNIKA

Aug 29, 2020

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