EOG biofeedback protocol based on selecting distinctive features to treat or reduce ADHD symptoms

Abstract

There are various evidences about existence of a difference between the electrooculography (EOG) signals of healthy individuals and that of people with attention deficit hyperactivity disorder (ADHD). Therefore, the use of EOG biofeedback might be effective in the decrease in ADHD symptoms. Therefore, the present study aimed to propose an EOG biofeedback protocol to treat ADHD. To this end, a set of diverse features of EOG signals was extracted in two groups of healthy children and subjects with ADHD during a focused attention task. Afterwards, the most effective features in distinguishing the EOG signals of the two groups were determined using the genetic algorithm. According to the results of the study, the values of low-frequency (0.5–4.125 Hz) band power, entropy, and fractal dimension were significantly lower in the EOG signals of the ADHD group, compared to the healthy children (P < .001). In addition, the scaling exponent was significantly higher in this regard (P < .001). Furthermore, entropy and fractal dimension were selected as the most effective features in distinguishing the two groups. It is recommended that entropy and fractal dimensions of EOG signals be increased as a protocol to treat or reduce ADHD symptoms.