# Deep Learning for EEG-Based Preference Classification in Neuromarketing
## 1 Introduction  
Neuromarketing is an emerging field that links the cognitive and affective sides of the consumer behavior by using neuroscience. Since its origin in 2002, this field has rapidly achieved credibility among the advertising and marketing specialists, and many such specialists are adopting neuromarketing strategies [1].  
Neuromarketing can assist marketers in understanding how a consumer's brain evaluates the diverse brands and recognizing the factors that affect the consumers' choices when purchasing products. Neuromarketing research has demonstrated that people do not always recognize whathappens in their unconscious brains. Furthermore, it has been demonstrated that people are not always explicit in their preferences or intentions [2].  
The use of traditional marketing tools, such as interviews and questionnaires, to assess consumer preferences, needs, and buying intentions can lead to the generation of biased or incorrect conclusions [3,4]. Similarly, an oral expression of preferences can produce conscious or unconscious biases. It is difficult to extract the consumer preferences directly through choices, owing to the high product costs, ethical caution considerations, or the product not having been invented at the time of evaluation [3]. These elements highlight a contradiction in the users' opinions during the usability assessments and their actual opinions, feelings, and senses regarding the use of a product [4].  
Therefore, neuromarketing requires more effective methodological alternatives to evaluate the consumer behavior. Novel neuroimaging procedures provide an effective approach to study consumer behavior. Such methods ultimately help marketers examine the consumers' brains to obtain valuable insights into the subconscious procedures underlying successful or failed marketing messages. This information is obtained by eliminating the primary problem in traditional advertising research, that is, trusting people; in particular, people should be trusted, whether they are consumers or workers who report on how the consumers are influenced by a specific part of an advertisement [1].  
Brain-computer interfaces (BCIs) are promising neuroimaging tools in neuromarketing. This technology allows the users to communicate effectively with computer systems. A BCI does not require the use of any external devices or muscle interference to produce commands [5]. Furthermore, a BCI employs voluntarily generated user brain activity to control a system through signals, which provides the ability to communicate or interact with the nearby environment. Electroencephalography (EEG) is one of the main instruments used to examine brain activity. The EEG technique is the only practical, versatile, affordable, portable, non-invasive BCI to perform repetitive, real-time analysis of brain interactions in high temporal resolution [5, 6, 7].  
Therefore, in the present research study, EEG was adopted as the input brain signal for a BCI system. Using classification algorithms, BCIs can be used as neural measures to distinguish the preference patterns from brain signals and translate them into actions to promote a product. In addition, the performance of a deep neural network (DNN) was implemented and examined to model a benchmark dataset for the preference classification.  
The main objective of this research was to deeply investigate EEG-based preference recognition in neuromarketing to enhance the accuracy of classification prediction by comparing the performance of deep-learning with other conventional classification algorithms, such as support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN).