applied sciences Article Deep Learning for EEG-Based Preference Classification in Neuromarketing Mashael Aldayel 1,2,*,† , Mourad Ykhlef 1,† and Abeer Al-Nafjan 3,† 1 Information System Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia;
[email protected] 2 Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia 3 Computer Science Department, College of Computer and Information Sciences, Imam Muhammad bin Saud University, Riyadh 11432, Saudi Arabia;
[email protected] * Correspondence:
[email protected] † Current address: Riyadh 11432, Saudi Arabia. Received: 14 January 2020; Accepted: 19 February 2020; Published: 24 February 2020 Featured Application: This article presents an application of deep learning in preference detection performed using EEG-based BCI. Abstract: The traditional marketing methodologies (e.g., television commercials and newspaper advertisements) may be unsuccessful at selling products because they do not robustly stimulate the consumers to purchase a particular product. Such conventional marketing methods attempt to determine the attitude of the consumers toward a product, which may not represent the real behavior at the point of purchase. It is likely that the marketers misunderstand the consumer behavior because the predicted attitude does not always reflect the real purchasing behaviors of the consumers. This research study was aimed at bridging the gap between traditional market research, which relies on explicit consumer responses, and neuromarketing research, which reflects the implicit consumer responses. The EEG-based preference recognition in neuromarketing was extensively reviewed. Another gap in neuromarketing research is the lack of extensive data-mining approaches for the prediction and classification of the consumer preferences.