Open AccessCLASSIFICATION OF BRAIN SIGNALS FOR RPAS CONTROL IN THE TREATMENT OF ATTENTION DEFICIT HYPERACTIVITY DISORDER
Author(s) -
Alejandro Sanchez-Carmona,
Carmelo Javier Villanueva Cañizares,
Álvaro Gómez Rodríguez,
Luis García Hernández,
Cristina Cuerno Rejado
Publication year - 2021
Publication title -
dyna
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.177
H-Index - 11
eISSN - 1989-1490
pISSN - 0012-7361
DOI - 10.6036/9496
Subject(s) - neurofeedback , electroencephalography , brain–computer interface , attention deficit hyperactivity disorder , computer science , interface (matter) , signal (programming language) , controller (irrigation) , artificial intelligence , psychology , neuroscience , psychiatry , bubble , maximum bubble pressure method , parallel computing , programming language , agronomy , biology
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.