Detection of Olfactory Stimulus in Electroencephalogram Signals Using Machine and Deep Learning Methods

Abstract

The 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.

Cite this article as: B. Akbugday, S. Pehlivan Akbugday, R. Sadikzade, A. Akan and S. Unal, "Detection of olfactory stimulus in electroencephalogram signals using machine and deep learning methods," Electrica, 24(1), 175-182, 2024.