UDC 004.942
MODELING OF NEUROFEEDBACK TRAINING SYSTEM BASED ON PORTABLE EEG FOR REHABILITATION OF PATIENTS WITH POST-TRAUMATIC STRESS DISORDER
M. S. Galushka, post-graduate student, Institute of Nanotechnologies, Electronics and Equipment Engineering, Southern Federal University, Taganrog, Russia;
orcid.org/0009-0000-3550-9518, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
V. Yu. Vishnevetskiy, PhD (in technical sciences), Associate Professor, Department of Electronic Devices and Medical Engineering Technologies, Institute of Nanotechnologies, Electronics and Equipment Engineer ing, Southern Federal University, Taganrog, Russia; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
The aim of this work is to develop and formally describe mathematical and structural models of key components for a biofeedback (BFB) system based on portable electroencephalography (EEG) for the reha bilitation of patients with post-traumatic stress disorder (PTSD). The relevance of the study is due to the growing prevalence of PTSD and the need to create personalized, accessible, and effective methods for its correction. The article addresses the tasks of modeling the process for selecting an initial neurofeedback training protocol based on the integration of clinical and EEG data, an algorithm for processing single channel EEG signals to extract relevant neurophysiological markers, and a biocontrol loop that implements neurofeedback. A protocol selection model based on a production rule system, considering patient's clinical profile (determined by PCL-5) and EEG patterns (identified from individual resting-state recordings), is pre sented. A detailed model for EEG signal processing is provided, including preprocessing stages (band-pass and notch filtering, artifact detection and rejection), spectral analysis using Welch's method, and calculation of power characteristics for alpha, beta, and theta rhythms. A structural and functional model of neurofeed back training loop is developed, describing the interaction between a patient and hardware-software com plex, as well as threshold adaptation algorithms based on target performance. The obtained models create a theoretical basis for the development of algorithmic and software solutions for personalized BFB systems for PTSD treatment, providing a formalized description of their functioning and the possibility for further verifi cation and optimization. The modeling results can be used in creating software for portable EEG devices aimed at improving the psycho-emotional state of patients with PTSD.
Key words: post-traumatic stress disorder, biofeedback, electroencephalography, portable devices, neu rofeedback, signal processing, control system, adaptive algorithm, rehabilitation.