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Clin EEG Neurosci. 2015 Oct;46(4):321-6. doi: 10.1177/1550059414523764. Epub 2014 Apr 14.

Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach.

Author information

  • 1Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul, Turkey.
  • 2Department of Psychiatry, NPIstanbul Hospital, Istanbul, Turkey Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.
  • 3Department of Bioengineering, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul, Turkey.


Feature selection is an important step in many pattern recognition systems aiming to overcome the so-called curse of dimensionality. In this study, an optimized classification method was tested in 147 patients with major depressive disorder (MDD) treated with repetitive transcranial magnetic stimulation (rTMS). The performance of the combination of a genetic algorithm (GA) and a back-propagation (BP) neural network (BPNN) was evaluated using 6-channel pre-rTMS electroencephalographic (EEG) patterns of theta and delta frequency bands. The GA was first used to eliminate the redundant and less discriminant features to maximize classification performance. The BPNN was then applied to test the performance of the feature subset. Finally, classification performance using the subset was evaluated using 6-fold cross-validation. Although the slow bands of the frontal electrodes are widely used to collect EEG data for patients with MDD and provide quite satisfactory classification results, the outcomes of the proposed approach indicate noticeably increased overall accuracy of 89.12% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.904 using the reduced feature set.

© EEG and Clinical Neuroscience Society (ECNS) 2014.


artificial neural network; cordance; genetic algorithm; major depressive disorder; rTMS

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