Analysis and classification of EEG signals recorded during different mental tasks for a brain computer interface design

In this study, features of EEG signals which are recorded during different mental and motor tasks are extracted and the performances of those features are tested with two different classifiers. EEG signals recorded during mental tasks are analyzed so that it is aimed to help people with neurophysiological disorders. The tasks are relax state, problem solving, imagination of right hand, imagination of left hand and imagination of the letter A.A Biosemi ActiveTwo System is used to record EEG signals and the data gathered from the chosen nine electrode channels are transferred to the MATLAB for analysis and then purified from noise and outliers. After that a two step feature extraction algorithm is applied. The first step involves calculating power spectral densities by Welch method, and the second step is developed from this power spectral density data, which is determining alpha and beta band characteristics as features. The extracted feature vectors are transferred to Linear Discriminant Analysis and Support Vector Machines classifiers and they are used for two class and multiclass classification. Classification results are obtained for each channel and by using those results the most active channels for each task are determined.Among the classifiers, Support Vector Machines performed better than the Linear Discriminant Analysis for both classification schemes. In addition the use of four electrodes found enough for analysis. The achieved classification results showed the eligibility of five different tasks for Brain Computer Interface applications. Finally, the classification performances are considered by classification accuracy, sensitivity and specificity constraints.