In neuromarketing studies, researchers use biometric responses such as facial expression (Filipović et al., 2020), eye tracking (Khushaba et al., 2013), functional magnetic resonance imaging (fMRI) (Hsu and Cheng, 2018), galvanic skin response (Ohira and Hirao, 2015), and electroencephalography (EEG) (Golnar-Nik et al., 2019), magnetoencephalograpy (MEG) (Hege et al., 2014) to extract customers' insights. In the last 20 years, researchers proposed several automatic approaches with some of these considering the neurological mechanisms that drive marketing decision-making and contribute to the rapidly expanding field of neuromarketing research. As a result, there is a need for technology-enabled autonomous prediction of consumer preferences. Neuromarketing, on the other hand, solves these issues by focusing on capturing the in-person response by taking into account brain response. Although these approaches are simple, they oftentimes fail to reflect the true state of mind of the customers, primarily because of the limitations associated with self-reported questionnaire surveys (Rawnaque et al., 2020). Traditional research methods rely on consumers filling out questionnaires, focus group discussion, or one-on-one interviews to determine their attitudes toward products, mostly on post-facto basis (Hulland et al., 2018). Hence, there is a significant motivation to investigate opportunities for targeting the appropriate market segments and customers. They spend $750 billion or more every year on marketing, promotion, and advertising to achieve this (Guttmann, 2021). Furthermore, one of the main objectives of marketing professional is to present their advertisement in such a way that the intended consumer response is elicited. Neuromarketing uses Brain-Computer Interface (BCI) technologies to gain insight into consumers' preferences and purchase intention triggered by marketing stimuli. It is an emerging multidisciplinary area that brings together neuroscience, technology, and traditional marketing. Neuromarketing is a subfield of marketing research that investigates customers' cognitive and emotive responses to promoted products or services. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Moreover, negative AA signals shows more dispersion than positive AA signals. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. The experimental results show that proposed framework achieves an accuracy of 84 and 87.00% for PI and AA ensuring the simulation of real-life results. Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) and affective attitude (AA) from analyzing EEG signals. On the other hand, Neuromarketing promises to overcome such constraints. They use traditional marketing research procedures such as Personal Depth Interviews, Surveys, Focused Group Discussions, and so on, which are frequently criticized for failing to extract true consumer preferences. Marketers spend about $750 billion annually on traditional marketing camping. Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how customers react to marketing stimuli.
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