EEG-based classification of new imagery tasks using three-layer feedforward neural network classifier for brain-computer interface

In this paper proposes the classification method of new imagery tasks for simple binary commands approach to a brain-computer interface (BCI). An analysis of imaginary tasks as "yes/no" have been proposed. Since BCI is very helpful technology for the patients who are suffering from severe motor disabilities. The BCI applications can be realized by using an electroencephalogram (EEG) signals recording at the scalp surface through the electrodes. Six healthy subjects (three males and three females), aged 23-30 years, were volunteered to participate in the experiment. During the experiment, 10-questions were used to be stimuli. The feature extraction of the event-related synchronization and event-related desynchronization (ERD/ERS) responses can be determined by the slope coefficient and Euclidian distance (SCED) method. The method uses the three-layer feedforward neural network based on a simple backpropagation algorithm to classify the two feature vectors. The experimental results of the proposed method show the average accuracy rates of 81.5 and 78.8% when the subjects imagine to 11 yes" and "no", respectively.