The Use of Electroencephalogram and Electrodermal Signals in Reinforcement Learning of a Brain-Computer Interface

Abstract

The objective of this work is to compare the performance of two brain-computer interfaces developed by our research group. Both interfaces collect the electrical signals produced by the human body while a person try to move a cursor on a digital screen, using only his thought. The collected signals are classified using the artificial neural networks paradigm, where the first interface uses electroencephalogram signals, collected from the scalp, to classify the mental command, and the second uses the electrodermal signal, collected from any right-hand finger. Besides analysing the performance of the two approaches, this research contributes to reduce the training time achieved by similar systems, reported in the literature as being in an average of 45 days, to about only 40 minutes. Our motivation is to facilitate the accessibility of people with temporary or permanent physical limitations. In addition, we have developed a low-cost signal collection platform, providing a solution that can help a large group of people.