Optimal EEG feature selection by genetic algorithm for classification of imagination of hand movement

Brain computer interface (BCI) system allows users to direct interact with the surrounded environment just a blink of their thought. In doing this, the most relevant informative from the electroencelography (EEG) signals need to be extracted from the electrodes of the scalp. Neurophysiology studies have proved that the power density from corresponding electrodes and frequency could identify imagination of the left or right hand movement. They also proved that these feature vary strongly from one subject to another. These spatial-time-frequency components are the keys to unlock the optimal features from the large space of these power density features. In this paper, we proposed the optimal feature extracting method from the basic power density of EEG signal. At first, the all EEG signal from the electrodes were filter using both spatial and temporal filters to enhance the signal to noise ratio of EEG. Then, the time-frequency features were extracted using short-time Fourier transform (STFT) and average power in sub-window band. Genetic algorithm was applied to search for the optimal features. In our simulation, we used the dataset from BCI competition III, IV and the data experimented in our laboratory. To ensure the improvement of our proposed feature extraction method, we applied the extracted feature into the support vector machine.