Predicting Consumer Choices Using Brain Signals and a Hybrid Gray Wolf–Cheetah Algorithm
Shan Ali Abdula
Department of Computer Science, University of Garmian, Iraq.
Hersh Fakhradin Aziz
Department of Computer Science, Darbandikhan Technical Institute, Sulaimani Polytechnic University, Iraq.
Rawezh Kamaran Ahmed
Department of Media, College of Human Science, University of Halabja, Iraq.
Mohammed Satar Saeed
Department of Islamic Study, Imam Aldham University, Iraq.
Twana Nasih Ahmed
Department of Network Systems and Services, Budapest University of Technology and Economics, Hungary.
Hemn Ghazi
Department of Business Administration, Sulaimani Technical Institute, Sulaimani Polytechnic University, Iraq.
Ali Mohammed Salih *
Department of Petrolume and Energy, Sulaimani Polytechnic University, Iraq.
*Author to whom correspondence should be addressed.
Abstract
The diversity of customer decision-making and product preferences combine to form a marketer's nightmare. In this work, we introduce a new neuromarketing method for predicting consumer choices based on brain signals. Participants were 25 healthy volunteers (1838 years old) who observed 14 items while their electroencephalogram (EEG) was recorded. In this paper, a hybrid feature selection method is proposed on the basis of the parallel population strategy of the Gray Wolf Optimizer and the Cheetah Optimization Algorithm. In contrast to the existing approaches, with this hybrid model the exploration ability of the GWO is combined with the exploitation rate of Cheetah Algorithm, where the convergence rate and the feature relevance on both of them are improved simultaneously. Spectra high order, a method capturing non-linear and complex pattern in EEG signals, was applied for the initial feature extraction, resulting 742 features. The proposed algorithm further yielded a reduced set of 174 key features. It was found that the average prediction accuracy of the model in predicting users’ product choices was 76.84%, 4.92% higher than that of baseline methods. These findings show the promise of this method in improving targeted advertising and creating a more personalized consumer experience.
Keywords: Adaptation models, feature selection, neuromarketing, meta-heuristic algorithm, machine learning