CLASSIFICATION OF BRAIN SIGNALS FOR RPAS CONTROL IN THE TREATMENT OF ATTENTION DEFICIT HYPERACTIVITY DISORDER

Abstract

The Attention Deficit Hyperactivity Disorder (ADHD) is characterized by a difficulty in processing feedback regarding the current state of the concentration of an individual. One of the main lines of research in the treatment of ADHD involved the employment of electroencephalography (EEG) Neurofeedback as a means of providing a quantification and representation of the concentration level. The current investigation constitutes a first step in developing an application of Remotely Piloted Aircraft Systems aiding in the treatment of ADHD employing a Brain Computer Interface, based on the measurements detected by an EEG sensor. These measurements modify the flight height of a quadrotor according to the signal evaluation. In order to develop the proposed system, a real-time mechanism for processing and classifying the electrophysiological artifacts has been developed. Finally, the processed signals are then fed into the aircraft controller, modifying the aircraft flight and thus providing the desired feedback to the user. Keywords: BCI; drone; RPAS; EEG; ADHD; Neurofeedback; machine learning; neural network.