Artificial neural networks in classification of the real and imaginary hand movements based on EEG signal

Krzysztof Chojnowski (1), Janusz Frączek (2)
(1) Institute of Radioelectronics, Faculty of Electronics and Information Technologies, Warsaw University of Technology
(2) Institute of Electronic Systems, Faculty of Electronics and Information Technologies, Warsaw University of Technology

BCI (called Brain-Computer Interface) is an interface that allows direct communication between human brain and an external device. One of the main uses of BCI is to help paralyzed or suffered from diseases people, who are not able to communicate in a normal way. It bases on EEG signal collection, processing and classification. In this paper a complete BCI system is presented which classifies EEG signal using artificial neural networks. For this purpose we used a multi-layered perceptron architecture trained with the RProp algorithm. Furthermore a simple multi-threaded method for automatic network structure optimizing was shown. We presented the results of our system in the opening and closing eyes recognition task. We also showed how our system could be used for controlling devices basing on imaginary hand movements.