Detection of Olfactory Stimulus from EEG Signals for Neuromarketing Applications

In this study, a method is proposed to detect the presence of olfactory stimuli from Electroencephalogram (EEG) signals to be used in neuromarketing applications. Odor is used in different ways in neuromarketing applications since it stimulates various emotions. Multi-channel EEG signals were recorded from the volunteers while they were subjected to two open boxes of unscented and scented products in succession. After the necessary preprocessing steps, EEG sub-band powers were calculated for 14 EEG channels. These features were classified using machine learning methods, and the EEG segments in which the olfactory stimulus was present were classified. The results show that the proposed method gives successful results with 92% accuracy, 93% precision, 92% recall, and 92% F1-score using the Random Forest classifier.