Exploring the attention process differentiation of attention deficit hyperactivity disorder (ADHD) symptomatic adults using artificial intelligence on electroencephalography (EEG) signals

Basit Öğe Kaydı
dc.authoridSaygili, Gorkem/0000-0002-9049-2138
dc.contributor.authorGuney, Gokhan
dc.contributor.authorKisacik, Esra
dc.contributor.authorKalaycioglu, Canan
dc.contributor.authorSaygili, Gorkem
dc.date.accessioned2025-02-24T17:19:08Z
dc.date.available2025-02-24T17:19:08Z
dc.date.issued2021
dc.departmentFakülteler, Fen-Edebiyat Fakültesi, Psikoloji Bölümü
dc.description.abstractAttention deficit and hyperactivity disorder (ADHD) onset in childhood and its symptoms can last up till adulthood. Recently, electroencephalography (EEG) has emerged as a tool to investigate the neurophysiological connection of ADHD and the brain. In this study, we investigated the differentiation of attention process of healthy subjects with or without ADHD symptoms under visual continuous performance test (VCPT). In our experiments, artificial neural network (ANN) algorithm achieved 98.4% classification accuracy with 0.98 sensitivity when P2 event related potential (ERP) was used. Additionally, our experimental results showed that fronto-central channels were the most contributing. Overall, we conclude that the attention process of adults with or without ADHD symptoms become a key feature to separate individuals especially in fronto-central regions under VCPT condition. In addition, using P2 ERP component under VCPT task can be a highly accurate approach to investigate EEG signal differentiation on ADHD-symptomatic adults.
dc.identifier.doi10.3906/elk-2011-3
dc.identifier.endpage2325
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85117245977
dc.identifier.scopusqualityQ2
dc.identifier.startpage2312
dc.identifier.trdizinid524252
dc.identifier.urihttps://doi.org/10.3906/elk-2011-3
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/524252
dc.identifier.urihttps://hdl.handle.net/20.500.14440/1017
dc.identifier.volume29
dc.identifier.wosWOS:000703667100004
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherTubitak Scientific & Technological Research Council Turkey
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250201
dc.subjectAttention deficit hyperactivity disorder
dc.subjectartificial neural network
dc.subjectsupport vector machine
dc.subjectelectroen-cephalography
dc.subjectvisual continuous performance test
dc.titleExploring the attention process differentiation of attention deficit hyperactivity disorder (ADHD) symptomatic adults using artificial intelligence on electroencephalography (EEG) signals
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Fen-Edebiyat Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
TR-Dizin İndeksli Yayınlar Koleksiyonu