EEG signal identification based on root mean square and average power spectrum by using BackPropagation

The development of user interface for game technology has currently employed human centered technology researches in which EEG signal that utilizes the brain function has become one of the trends. The present research describes the identification of EEG Signal by segmenting it into 4 different classes. The segmentation of these classes is based on Root Mean Square (RMS) and Average Power Spectrum (AVG), employed in feature extraction. Both Root Mean Square (RMS) and Average Power Spectrum (AVG) are employed to extract features of EEG signal data and then used for identification, by which a BackPropagation method is employed. The experiment, done with 200 tested signal data file, demonstrates that the identification of the signal is 91% accurate.