Neural Network-based Three-Class Motor Imagery Classification Using Time-Domain Features for BCI Applications

Many studies have reported the usefulness of motor imagery (MI) electroencephalogram (EEG) signals for Brain Computer Interface (BCI) systems. MI has been broadly characterized by the average of event-related changes of brain activity at specific frequency bands; but, temporal features of EEG have rarely been considered to identify different mental states of BCIs' users. Additionally, complex classification techniques may have been proposed to enhance the accuracy of system but they may cause a notable delay during online applications. This paper investigated the application of neural network-based algorithms to classify three-class MIs by utilizing EEG time-domain features. Integrated EEG (IEEG) and Root Mean Square (RMS) features were extracted from EEG signals. Then, Multilayer Perceptron and Radial Basis Function Neural Networks were employed to classify the features. The discrimination ratio of such features were examined and compared through different classifiers. Moreover, the robustness of classifiers was investigated and compared. The results of this study indicated that RMS was more capable than IEEG for characterizing MI movements and RBF was more accurate and faster than MLP. The effectiveness of IEEG and RMS features and the performance of MLP and RBF classifiers were compared with Willison Amplitude (WAMP) feature and support vector machine (SVM) classifier respectively. This study proved that WAMP and SVM were more efficient for classification of MI tasks in both terms of accuracy (88.96%) and training time (0.5 second); however, considerable difference was not observed since RBF performed as fast as SVM with only about 3% less accuracy.