An EEG Based Liking Status Detection Method for Neuromarketing Applications

In this study, an estimation system based on electroencephalogram (EEG) signals has been developed for use in neuromarketing applications. Determination of the degree of consumer liking a product by processing the biological data (EEG, facial expressions, eye tracking, Galvanic skin response, etc.) recorded while viewing the product images or videos has become an important research topic. In this study, 32-channel EEG signals were recorded from subjects while they watch two different car advertisement videos, and the liking status was determined. After watching the car commercial videos, the subjects were asked to vote on the rating of different images (front view, front console, side view, rear view, rear light, logo and front grill) of the cars. The signals corresponding to these different video regions from the EEG recordings were segmented and analyzed by the Empirical Mode Decomposition (EMD) method. Several statistical features were extracted from the resulting Intrinsic Mode Functions and the liking status classification was performed. Classification results obtained with Support Vector Machines (SVM) classifiers indicate that the proposed EEG-based liking detection method may be used in neuromarketing studies.