Asynchronous multiclass mental tasks classification through very fast Versatile Elliptic Basis Function Neural Network

Developing efficient and usable brain-computer interfaces (BCIs) requires well-designed trade-off between accuracy and computational time. This paper presents a very fast and accurate method to classify asynchronous brain signals from a multi-class mental tasks dataset using time-domain features. Five different statistical time-domain features were extracted to characterize various properties of three mental tasks electroencephalograms (EEGs). Versatile Elliptic Basis Function Neural Network (VEBFNN) was employed to classify single EEG features as well as multi-feature set. Discriminating power of single features was evaluated and compared by considering the classification accuracy and computational cost consumed during the training stage. Finally, the performance of the best single EEG feature was compared to the multi-feature set. The results indicated the usefulness of Willison Amplitude EEG feature in classifying the different motor tasks as it provided the highest discrimination ratio. Classification results showed the high potential of VEBFNN by the average 89.78% accuracy and 0.21 seconds computation time obtained for its offline training. Moreover, VEBFNN outperformed the conventional support vector machine classifier in both terms of accuracy and speed.