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

Khushaba, RN; Greenacre, L; Kodagoda, S; Louviere, J; Burke, S; Dissanayake, G

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

Khushaba, RN

Greenacre, L

Kodagoda, S

Louviere, J

Burke, S Dissanayake, G

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Publication Type:: Journal Article
Citation:: Expert Systems with Applications, 2012, 39 (16), pp. 12378 - 12388
Issue Date:: 2012-11-15

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Field	Value	Language
dc.contributor.author	Khushaba, RN https://orcid.org/0000-0001-8528-8979	en_US
dc.contributor.author	Greenacre, L https://orcid.org/0000-0002-6029-6523	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.contributor.author	Louviere, J https://orcid.org/0000-0001-7688-164X	en_US
dc.contributor.author	Burke, S	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2012-11-15	en_US
dc.identifier.citation	Expert Systems with Applications, 2012, 39 (16), pp. 12378 - 12388	en_US
dc.identifier.issn	0957-4174	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22019
dc.description.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 (0.5-4 Hz), theta (4-7 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. 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. © 2012 Elsevier Ltd. All rights reserved.	en_US
dc.relation.ispartof	Expert Systems with Applications	en_US
dc.relation.isbasedon	10.1016/j.eswa.2012.04.084	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Choice modeling and the brain: A study on the Electroencephalogram (EEG) of preferences	en_US
dc.type	Journal Article
utslib.citation.volume	16	en_US
utslib.citation.volume	39	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Strength - CENSOC - Study of Choice
pubs.organisational-group	/University of Technology Sydney/Strength - CMOS - Centre for Management and Organisation Studies
utslib.copyright.status	closed_access
pubs.issue	16	en_US
pubs.publication-status	Published	en_US
pubs.volume	39	en_US

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 (0.5-4 Hz), theta (4-7 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. 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. © 2012 Elsevier Ltd. All rights reserved.

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http://hdl.handle.net/10453/22019