Feasibility of detecting ADHD patients' attention levels by classifying their EEG signals

Attention Deficit Hyperactivity Disorder (ADHD), characterized by the lack of attention and focus, is one of the most spread cognitive disorders. Since electroencephalogram (EEG) signals carry extensive information about cognition skills, which include attention, then the potential of using EEG signals for people with low attention span can be quite significant. EEG can be read using the new wireless EEG reading devices often used by Brain-computer Interface (BCI) researchers. In parallel, serious games have been recently utilized for rehabilitating various cognitive and emotional deficits. In this paper, we put the two things together, and we investigate the integration of an EEG-controlled serious game that trains and strengthens patients' attention ability while using machine learning to detect their attention level. Our preliminary experiments with healthy individuals show an accuracy of up to 96% in classifying the EEG data to detect the correct attention state during gameplay. This promising result serves as motivation to test our models with actual ADHD patients in the future.