Background: Neurofeedback is a non-invasive brain training technique used to enhance and treat hyperactivity disorder by altering the patterns of brain activity. Nonetheless, the extent of enhancement by neurofeedback varies among individuals/patients and many of them are irresponsive to this treatment technique. Therefore, several studies have been conducted to predict the effectiveness of neurofeedback training including the theta/beta protocol with a specific emphasize on slow cortical potential (SCP) before initiating treatment, as well as examining SCP criteria according to age and sex criteria in diverse populations. While some of these studies failed to make accurate predictions, others have demonstrated low success rates. This study explores functional connections within various brain lobes across different frequency bands of electroencephalogram (EEG) signals and the value of phase locking is used to predict the potential effectiveness of neurofeedback treatment before its initiation. Methods: This study utilized EEG data from the Mendelian database. In this database, EEG signals were recorded during neurofeedback sessions involving 60 hyperactive students aged 7–14 years, irrespective of sex. These students were categorized into treatable and non-treatable. The proposed method includes a five-step algorithm. Initially, the data underwent preprocessing to reduce noise using a multi-stage filtering process. The second step involved extracting alpha and beta frequency bands from the preprocessed EEG signals, with a particular emphasis on the EEG recorded from sessions 10 to 20 of neurofeedback therapy. In the third step, the method assessed the disparity in brain signals between the two groups by evaluating functional relationships in different brain lobes using the phase lock value, a crucial data characteristic. The fourth step focused on reducing the feature space and identifying the most effective and optimal electrodes for neurofeedback treatment. Two methods, the probability index (p-value) via a t-test and the genetic algorithm, were employed. These methods showed that the optimal electrodes were in the frontal lobe and central cerebral cortex, notably channels C3, FZ, F4, CZ, C4, and F3, as they exhibited significant differences between the two groups. Finally, in the fifth step, machine learning classifiers were applied, and the results were combined to generate treatable and non-treatable labels for each dataset. Results: Among the classifiers, the support vector machine and the boosting method demonstrated the highest accuracy when combined. Consequently, the proposed algorithm successfully predicted the treatability of individuals with hyperactivity in a short time and with limited data, achieving an accuracy of 90.6% in the neurofeedback method. Additionally, it effectively identified key electrodes in neurofeedback treatment, reducing their number from 32 to 6. Conclusions: This study introduces an algorithm with a 90.6% accuracy for predicting neurofeedback treatment outcomes in hyperactivity disorder, significantly enhancing treatment efficiency by identifying optimal electrodes and reducing their number from 32 to 6. The proposed method enables the prediction of patient responsiveness to neurofeedback therapy without the need for numerous sessions, thus conserving time and financial resources.
