Firefly Algorithm Based Feature Selection for EEG Signal Classification

Brain-computer interfaces (BCIs) recognize specific features of a person’s brain signal relating to his/her intent, and output a control command that controls the outside devices or computers. BCI systems facilitate the lives of patients who cannot move any muscles but have no cognitive disorder. The high dimensions of features represent a research challenge. In recent years, especially nature inspired heuristic optimization algorithms became popular in order to eliminate unnecessary features. This paper addresses a crucial factor for effective classification of motor imaginary based EEG signals that are an optimal selection of relevant EEG features using firefly algorithm. Firefly algorithm (FA) works on the principle of directing the less shiny than the light intensity emitted by fireflies in nature towards the bright. The algorithm can adaptively select the best subset of features and improve classification accuracy. In this study, following extracted Katz Fractal Dimension based features, effective feature(s) were selected by FA. The proposed method successfully applied on open access dataset which was collected from 29 subjects. We obtained an average 76.14% classification accuracy (CA) using k-nearest neighbor classifier. This is 4.4% higher than the CA calculated by using all features. These results proved that used method is robust for this dataset.