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Attention deficit hyperactivity disorder (ADHD), categorized by the lack of attention and focus, is one of the most common cognitive disorders. Since electroencephalogram (EEG) signals carry wide-ranging insights about cognition skills, the potential of using EEG signals to detect ADHD has a significant potential. EEG can be recorded utilizing wireless EEG reading devices often used by brain-computer interface researchers. In parallel-to-affordable EEG devices, serious games have been recently employed in the rehabilitation of multiple cognitive 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 pilot experiments with healthy individuals show an accuracy of up to 96% in classifying the EEG data to detect the correct game control type during gameplay, while our extended experiments with ADHD patients show an accuracy of up to 98% with a standard uncertainty of 0.16% in detecting ADHD patients.
Alchalabi et al. (Fri,) studied this question.