Can we predict who will respond more to neurofeedback with resting state EEG?

AutoTrainBrain is a neurofeedback and multi sensory learning-based mobile phone software application, designed at Sabanci University with the aim of improving the cognitive functions of dyslexic children. We investigated whether we can predict who will respond more to neurofeedback applied by AutoTrainBrain by analyzing the resting state EEG brain data. Based on our analysis of the EEG data collected, we observed that the power amplitudes across resting states in the theta band over the left Dorsolateral Prefrontal Cortex (DLPFC) (electrode: FC5) predicts who will respond more to neurofeedback with AutoTrainBrain (Pearson correlation coeff: 0.78, P<0.001). When we reduce the high theta brain waves with neurofeedback in this area, we hypothesize that better cortical regulation and inhibition are developed in the brain, therefore the response to neurofeedback increases.