Choice modeling and the brain: A study on the Electroencephalogram (EEG) of preferences

  • Authors:

  • Rami N. Khushaba;Luke Greenacre;Sarath Kodagoda;Jordan Louviere;Sandra Burke;Gamini Dissanayake

  • Affiliations:

  • Center of Intelligent Mechatronics Systems (CIMS), Faculty of Engineering and Information Technology, University of Technology, Sydney (UTS), Australia;Centre of Study of Choice (CenSoc), University of Technology, Sydney (UTS), Australia;Center of Intelligent Mechatronics Systems (CIMS), Faculty of Engineering and Information Technology, University of Technology, Sydney (UTS), Australia;Centre of Study of Choice (CenSoc), University of Technology, Sydney (UTS), Australia;Centre of Study of Choice (CenSoc), University of Technology, Sydney (UTS), Australia;Center of Intelligent Mechatronics Systems (CIMS), Faculty of Engineering and Information Technology, University of Technology, Sydney (UTS), Australia

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2012

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Abstract

Choice conjures the idea of a directed selection of a desirable action or object, motivated by internal likes and dislikes, or other such preferences. However, such internal processes are simply the domain of our human physiology. Understanding the physiological processes of decision making across a variety of contexts is a central aim in decision science as it has a great potential to further progress decision research. As a pilot study in this field, this paper explores the nature of decision making by examining the associated brain activity, Electroencephalogram (EEG), of people to understand how the brain responds while undertaking choices designed to elicit the subjects' 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. These choice sets were composed of three images offering potential personal computer backgrounds. Choice based preferences were identified by having the respondent click on their preferred one. In addition, a brain computer interface (BCI) represented by the commercial Emotiv EPOC 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 main principal frequency bands, delta (0.5-4Hz), theta (4-7Hz), alpha (8-12Hz), beta (13-30Hz), and gamma (30-40Hz). A mutual information (MI) measure was then used to study left-to-right hemisphere differences as well as front-to-back difference. Eighteen participants were recruited to perform the experiments with the average results showing clear and significant change in the spectral activity in the frontal (F3 and F4), parietal (P7 and P8) and occipital (O1 and O2) areas while the participants were indicating their preferences. The results show that, when considering the amount of information exchange between the left and right hemispheres, theta bands exhibited minimal redundancy and maximum relevance to the task at hand when extracted from symmetric frontal, parietal, and occipital regions while alpha dominated in the frontal and parietal regions and beta dominating mainly in the occipital and temporal regions.