  In electroencephalogram (EEG) signal processing, finding the appropriate
information from a dataset has been a big challenge for successful signal
classification. The feature selection methods make it possible to solve this
problem; however, the method selection is still under investigation to find out
which feature can perform the best to extract the most proper features of the
signal to improve the classification performance. In this study, we use the
genetic algorithm (GA), a heuristic searching algorithm, to find the optimum
combination of the feature extraction methods and the classifiers, in the
brain-computer interface (BCI) applications. A BCI system can be practical if
and only if it performs with high accuracy and high speed alongside each other.
In the proposed method, GA performs as a searching engine to find the best
combination of the features and classifications. The features used here are
Katz, Higuchi, Petrosian, Sevcik, and box-counting dimension (BCD) feature
extraction methods. These features are applied to the wavelet subbands and are
classified with four classifiers such as adaptive neuro-fuzzy inference system
(ANFIS), fuzzy k-nearest neighbors (FKNN), support vector machine (SVM) and
linear discriminant analysis (LDA). Due to the huge number of features, the GA
optimization is used to find the features with the optimum fitness value (FV).
Results reveal that Katz fractal feature estimation method with LDA
classification has the best FV. Consequently, due to the low computation time
of the first Daubechies wavelet transformation in comparison to the original
signal, the final selected methods contain the fractal features of the first
coefficient of the detail subbands.
