EEG Based Cigarette Addiction Detection with Deep Learning
TRAITEMENT DU SIGNAL, cilt.41, sa.3, ss.1183-1192, 2024 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 41 Sayı: 3
- Basım Tarihi: 2024
- Doi Numarası: 10.18280/ts.410308
- Dergi Adı: TRAITEMENT DU SIGNAL
- Derginin Tarandığı İndeksler: Scopus, Science Citation Index Expanded (SCI-EXPANDED), PASCAL, Business Source Elite, Business Source Premier, Compendex, zbMATH
- Sayfa Sayıları: ss.1183-1192
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Yozgat Bozok Üniversitesi Adresli: Evet
Özet
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 Fagerström 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.