Predicting the success rate of healthy participants in beta neurofeedback: Determining the factors affecting the success rate of individuals

• Published in: Biomedical signal processing and control Vol. 69; p. 102753
• Main Author: Sho’ouri, Nasrin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2021
• Subjects: Beta neurofeedback; EEG; Path diagram; RBF; Relative power in different frequency bands; Success rate prediction; Path diagram; Beta neurofeedback; EEG; Success rate prediction; Relative power in different frequency bands; RBF
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2021.102753

•EEG relative power in different frequency bands predicts individual’s success rate in neurofeedback training.•Initial low beta band activity predict success rate of individuals in beta neurofeedback.•The beta power and the score in the last session along with beta trend line were predicted. Despite the considerable success of neurofeedback techniques in the treatment of various neurological disorders and the improvement of cognitive performance of healthy individuals, some people fail to learn how to control their brain activities using neurofeedback. Given the time-consuming and costly nature of neurofeedback, the prediction of people’s success rate in training by neurofeedback is of paramount importance. Therefore, the present study aimed to determine the factors affecting the success rate of 7 healthy women over 10 sessions (30 trials) in terms of enhancement of low beta band activities (βL). The relative power of different frequency bands (delta, theta, alpha and beta) of EEG signals obtained in the first training session was considered as the predictor variable along with the participants’ IQ test score. Afterwards, we assessed the predictor variables’ impact on the mean low beta power (15–18 Hz) values of the participants’ EEG signals in the last session (βL(last sess)). According to the results, the mean low beta power in the first session (βL(sess1)) had the most effect on βL(last sess) (R2 = 73.9 %). In the next stage, we designed three systems using the RBF network, which predicted the βL(last sess), mean score of each participant in the last training session and the slope of βL changes of each subject during the training sessions using βL(sess1) (prediction error < 10−11). The designed prediction system may be able to increase training efficiency with neurofeedback and save time and financial resources.