PhyDAA: Physiological Dataset Assessing Attention
Victor Delvigne, Hazem Wannous, Thierry Dutoit, Laurence Ris, Jean‐Philippe Vandeborre
Abstract
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 or are not effective for all subgroups. Neurofeedback (NF) is an innovative treatment consisting of brain activity display. NF training could consist of a virtual reality (VR) video-game in which the participant’s attention affects the game. Attention being assessed through physiological signals, one of the main steps is to design an estimator for the attention state. We present a novel framework able to record physiological signals in specific attention states and able to estimate the corresponding attention state. We propose a database composed of electroencephalography signals (EEG), and an eye-tracker labelled with a score representing the attention span for 32 healthy participants. Different features are extracted from the signals and machine learning (ML) algorithms are proposed. Our approach exhibits high accuracy for attention estimation, which corroborates a correlation between attention state and physiological signals (i.e. EEG, eye-tracking signals). The dataset has been made publicly available to promote research in the domain and we encourage other scientists to use their own approach for attention estimation.