BRAIN-COMPUTER INTERFACE TECHNOLOGIES AND NEUROFEEDBACK - Al-Karawi Raad Joudat Salman; Al-Duhaidahawi Murtadha Ahmed Luti; Dhahir A. abdulah salman (Western European Studies, 2024)
The issues of using neurofeedback for various diseases and disorders of the functional state of the brain in children and adults are considered. In addition, the presentation of one's bioelectric potentials of the brain allows one to learn to control physiological functions controlled by a person unconsciously. Biofeedback (BFB) sessions based on EEG parameters influence fundamental rhythmic mechanisms by changing the neuro-modulatory influences of the brain stem, the plasticity of neural networks, and the formation of new neural ensembles. By changing the level and degree of EEG activity, this training normalizes activation mechanisms, thereby improving cortical stability. As a result of learning to control central regulatory mechanisms, EEG feedback sessions lead to the stabilization of the functioning of the nervous system as a whole. This plays a big role in increasing the functional abilities of the brain in both children and adults. The most effective use of this method is for epilepsy, various types of addictive disorders, sleep disorders in adults, and attention deficit hyperactivity disorder in children; it has also been successfully introduced into the training process of athletes. However, despite numerous studies, questions regarding the effective use of neurofeedback remain unresolved. In particular, researchers have not developed a consensus on the number, duration, and frequency of neurofeedback sessions. It is also important to study the problems of motivation and take into account the cognitive capabilities of the subjects in understanding tasks during the EEGBS training. The study of the dynamics of the spectral characteristics of the main rhythms during the EEG training course, and the assessment of the self-regulation strategy after completion of the biofeedback course based on EEG parameters will serve as the basis for the development of methodological recommendations for the development of skills for effective self-regulation of mental functions
Keywords: Brain-computer interface; Neurofeedback; EEG