A neuroscientific approach to choice modeling: Electroencephalogram (EEG) and user preferences

Abstract

Discrete choice experiments have traditionally focused on improving the prediction of static choices that are measured through external reflection and surveys. It is argued that considering the underlying processes of decision making across a variety of contexts may further progress decision research. As a pilot study in this field, this paper explores the dynamic nature of decision-making by examining the associated brain activity, Electroencephalogram (EEG), of people while undertaking choices designed to elicit their preferences. To facilitate such a study, the Tobii-Studio eye tracker system was utilized to capture the participants' choice based preferences when they were observing seventy two sets of objects of three images offering potential personal computer backgrounds. Choice based preferences were identified by having the respondent click on their preferred image. In addition, the commercial Emotiv wireless EEG headset with 14 channels was utilized to capture the associated brain activity during the period of the experiments. Principal Component Analysis (PCA) was utilized to preprocess the EEG data before analyzing it with the Fast Fourier Transform (FFT) to observe the changes in the four principal frequency bands, theta (3 - 7 Hz), alpha (8 - 12 Hz), beta (13 - 30 Hz), and gamma (30 - 40 Hz). A mutual information (MI) measure was then used to study left-to-right hemisphere differences as well as front-to-back difference. Across six recruited participants there was a clear and significant change in the spectral activities taking place mainly in the frontal (theta and alpha across F3 and F4) and occipital (alpha and beta across O1 and O2) regions while the participants were indicating their preferences.