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FOCUS: detecting adhd patients by an eeg-based serious game

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dc.contributor.author Alchalabi, A.E.
dc.contributor.author Shirmohammadi, Shervin
dc.contributor.author Eddin, A.N.
dc.contributor.author Elsharnouby, M.
dc.date.accessioned 2018-07-17T13:17:22Z
dc.date.available 2018-07-17T13:17:22Z
dc.date.issued 2018
dc.identifier.citation Alchalabi, A.E., Shirmohammadi, Shervin, Eddin, A.N., Elsharnouby, M. (2018). FOCUS: detecting adhd patients by an eeg-based serious game. IEEE Transactions on Instrumentation and Measurement, 67(7), pp. 1512-1520. en_US
dc.identifier.issn 0018-9456
dc.identifier.uri http://hdl.handle.net/11498/53885
dc.description.abstract 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 en_US
dc.language.iso eng en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation.isversionof 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. en_US
dc.rights info:eu-repo/semantics/embargoedAccess en_US
dc.subject Brain-Computer Interface en_US
dc.subject Brain-Controlled Games en_US
dc.subject Beyin-Bilgisayar Arayüzleri en_US
dc.subject Beyin Kontrollü Oyunlar en_US
dc.title FOCUS: detecting adhd patients by an eeg-based serious game en_US
dc.type Article en_US
dc.relation.journal IEEE Transactions on Instrumentation and Measurement en_US
dc.contributor.department Istanbul Şehir University. College of Engineering and Natural Sciences.Computer Science and Engineering. en_US
dc.identifier.volume 67 en_US
dc.identifier.issue 7 en_US
dc.identifier.endpage 1520 en_US
dc.identifier.startpage 1512 en_US


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