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EEG based cigarette addiction detection with deep learning
(Int Information & Engineering Technology Assoc, 2024) Cay, Talip; Ölmez, Emre; Altin, Cemil; Tanik, Nermin
In 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.