PhyDAA: Physiological Dataset Assessing Attention

Attention Deficit Hyperactivity Disorder (ADHD) is the most prevalent neurodevelopmental disorder among children. It affects patients’ lives in many ways: inattention, difficulty with stimuli inhibition or motor function regulation. Different treatments exist today, but these can present side effects are not effective for all subgroups. Neurofeedback (NF) an innovative treatment consisting of brain activity display. NF training could consist a virtual reality (VR) video-game which participant’s attention game. being assessed through physiological signals, one main steps to design estimator state. We novel framework able record signals specific states and estimate corresponding propose database composed electroencephalography (EEG), eye-tracker labelled score representing span 32 healthy participants. features extracted from machine learning (ML) algorithms proposed. Our approach exhibits high accuracy estimation, corroborates correlation between state (i.e. EEG, eye-tracking signals). The dataset has been made publicly available promote research domain we encourage other scientists use their own estimation.