Akbuğday, BurakAkbugday, S.P.Sadikzade, R.Akan, A.Unal, S.2024-03-302024-03-3020242619-9831https://doi.org/10.5152/electrica.2024.23111https://hdl.handle.net/20.500.14365/5216The investigation of olfactory stimuli has become more prominent in the context of neuromarketing research over the last couple of years. Although a few studies suggest that olfactory stimuli are linked with consumer behavior and can be observed in various ways, such as via electroencephalogram (EEG), a universal method for the detection of olfactory stimuli has not been established yet. In this study, 14-channel EEG signals acquired from participants while they were presented with 2 identical boxes, scented and unscented, were processed to extract several linear and nonlinear features. Two approaches are presented for the classification of scented and unscented cases: i) using machine learning (ML) methods utilizing extracted features; ii) using deep learning (DL) methods utilizing relative sub-band power topographic heat map images. Experimental results suggest that the olfactory stimulus can be successfully detected with up to 92% accuracy by the proposed method. Furthermore, it is shown that topographic heat maps can accurately depict the response of the brain to olfactory stimuli. © 2024 Istanbul University. All rights reserved.eninfo:eu-repo/semantics/closedAccessDeep Learningelectroencephalogram (EEG)machine learningneuro-marketingolfactory stimulusConsumer behaviorDeep learningLearning systemsDeep learningElectroencephalogramElectroencephalogram signalsHeat mapsLearning methodsMachine-learningNeuro-marketingNeuromarketingOlfactory stimulusUniversal methodElectroencephalographyArticle10.5152/electrica.2024.231112-s2.0-85185530677