Abstract
Brain Computer Interface (BCI) systems based on motor imagery tasks have significant usage for disabled people for their life style. Electroencephalogram (EEG) is one of the best approach to obtain human brain signals. The EEG signal requires three stages of preprocessing, feature extraction, and classification to increase signal analysis accuracy. Due to different task of brain, EEG distribution fluctuates (non-stationary), and therefore challenges BCI for many researchers. Recently a method named stationary subspace analysis (SSA) applied to some BCI data by some research teams in order to separate stationary and non-stationary parts of EEG. However they did not obtain significantly results. This method factorizes EEG into its stationary and non-stationary components by dividing signal into number of epochs, and compares their data distributions. In this study, we applied SSA in preprocessing stage into train and test data of the BCI competition dataset of nine healthy people. We applied the Genetic Algorithm (GA) to train our Artificial Neural Network (ANN) classifier. We also inspect different parameters for SSA to improve the performance. Our results indicate significant growth especially for subjects with worse results in other techniques (improving ~40% to ~70%). In addition, the mean of accuracies improves 5% in regard to the winner of competition.
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