  In this paper, a genetic algorithm-based frequency-domain feature search
(GAFDS) method is proposed for the electroencephalogram (EEG) analysis of
epilepsy. In this method, frequency-domain features are first searched and then
combined with nonlinear features. Subsequently, these features are selected and
optimized to classify EEG signals. The extracted features are analyzed
experimentally. The features extracted by GAFDS show remarkable independence,
and they are superior to the nonlinear features in terms of the ratio of
inter-class distance and intra-class distance. Moreover, the proposed feature
search method can additionally search for features of instantaneous frequency
in a signal after Hilbert transformation. The classification results achieved
using these features are reasonable, thus, GAFDS exhibits good extensibility.
Multiple classic classifiers (i.e., $k$-nearest neighbor, linear discriminant
analysis, decision tree, AdaBoost, multilayer perceptron, and Na\"ive Bayes)
achieve good results by using the features generated by GAFDS method and the
optimized selection. Specifically, the accuracies for the two-classification
and three-classification problems may reach up to 99% and 97%, respectively.
Results of several cross-validation experiments illustrate that GAFDS is
effective in feature extraction for EEG classification. Therefore, the proposed
feature selection and optimization model can improve classification accuracy.
