EEG Based Cigarette Addiction Detection with Deep Learning

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dc.authoridALTIN, Cemil/0000-0001-8892-2795
dc.contributor.authorCay, Talip
dc.contributor.authorOlmez, Emre
dc.contributor.authorAltin, Cemil
dc.contributor.authorTanik, Nermin
dc.date.accessioned2025-03-20T09:50:35Z
dc.date.available2025-03-20T09:50:35Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractIn this study, cigarette addiction detection was performed using machine learning techniques with time -frequency feature extraction methods on EEG data collected from 30 different male individuals. Electroencephalography (EEG) data collected from individuals who underwent the Fagerstr & ouml;m Test for Nicotine Dependence (FTND) were labeled as dependent or non-dependent based on their test results. The obtained EEG data were first subjected to Discrete Wavelet Transform (DWT). Then, Power Spectral Density (PSD) analysis and feature extraction processes were performed separately on the outputs obtained from the DWT process. The data obtained from PSD analysis and feature extraction processes were classified using Artificial Neural Networks (ANN). The aim of this study is to achieve higher success rates in cigarette addiction detection by classifying EEG data with machine learning methods after extracting time -frequency features, rather than using traditional methods. In this study, responses to cigarette stimuli were classified using machine learning methods based on EEG graphs. The results revealed that temporal and prefrontal lobes were more distinctive in responses to cigarette stimuli, and success rates were higher in the theta frequency band.
dc.identifier.doi10.18280/ts.410308
dc.identifier.endpage1192
dc.identifier.issn0765-0019
dc.identifier.issn1958-5608
dc.identifier.issue3
dc.identifier.scopusqualityN/A
dc.identifier.startpage1183
dc.identifier.urihttps://doi.org/10.18280/ts.410308
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2265
dc.identifier.volume41
dc.identifier.wosWOS:001260365800008
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherInt Information & Engineering Technology Assoc
dc.relation.ispartofTraitement Du Signal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250319
dc.subjectdiscrete wavelet transform power spectral
dc.subjectdensity nicotine dependence EEG deep
dc.subjectlearning artificial neural networks
dc.titleEEG Based Cigarette Addiction Detection with Deep Learning
dc.typeArticle

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