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

10.6036/9496 ◽  
2021 ◽  
Vol 96 (1) ◽  
pp. 220-224
Author(s):  
ALEJANDRO SANCHEZ CARMONA ◽  
CARMELO JAVIER VILLANUEVA CAÑIZARES ◽  
ALVARO GOMEZ RODRIGUEZ ◽  
LUIS GARCIA HERNANDEZ ◽  
CRISTINA CUERNO REJADO
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Hyperactivity Disorder ◽  
System A ◽  
Brain Signals ◽  
Current State ◽  
Remotely Piloted Aircraft ◽  
Signal Evaluation ◽  
Eeg Neurofeedback

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.

scholarly journals Diagnostic Support System for the Detection of Attention Deficit Hyperactivity Disorder Using EOG Signals

2021 ◽  
Author(s):  
Esra Demirci ◽  
Mustafa Yasin Esas ◽  
Çiğdem Gülüzar Altıntop ◽  
Neslihan Taştepe ◽  
Fatma Latifoğlu
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Support System ◽  
Attention Deficit ◽  
Diagnostic Methods ◽  
Healthy Individuals ◽  
Subjective Effect ◽  
Hyperactivity Disorder ◽  
Diagnostic Support

Abstract Although Attention Deficit Hyperactivity Disorder (ADHD) is a common childhood disease, objective diagnostic methods are insufficient still. Current diagnostic methods include the subjective influence of the evaluator. In this context, in our study, we aimed to minimize the subjective effect of the evaluator with the objective diagnosis support system for ADHD.In our study, a visual stimulus follow-up test developed by us was applied to the patient with ADHD and healthy individuals, and electrooculogram (EOG) signals were recorded simultaneously. With the features extracted from EOG signals, Artificial Neural Networks (ANN) were used for the classification study of patients and healthy individuals, and it was determined that the classification of ADHD and healthy group could be distinguished by 81.76% performance. Thus, the outcomes that will contribute to the objective diagnosis of ADHD have been presented. The results are remarkable and important findings have been obtained that will contribute to the literature.

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