FOCUS: Detecting ADHD Patients by an EEG-Based Serious Game

被引：71

作者：

Alchalabi, Alaa Eddin ^{[1
,2
]}

Shirmohammadi, Shervin ^{[1
,2
]}

Eddin, Amer Nour ^{[1
]}

Elsharnouby, Mohamed ^{[1
]}

机构：

[1] Istanbul Sehir Univ, Multimedia Syst Lab, TR-34865 Istanbul, Turkey

[2] Univ Ottawa, Distributed & Collaborat Virtual Environm Res Lab, Ottawa, ON K1N 6N5, Canada

来源：

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT | 2018年 / 67卷 / 07期

关键词：

Attention deficit hyperactivity disorder (ADHD) detection; brain-computer interface; brain-controlled games; electroencephalogram (EEG) classification; serious games; CLASSIFICATION;

D O I：

10.1109/TIM.2018.2838158

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：1512 / 1520

页数：9

共 32 条

[1] Decision Support Algorithm for Diagnosis of ADHD Using Electroencephalograms [J].