BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework

Abstract

Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how customers react to marketing stimuli. Marketers spend about $750 billion annually on traditional marketing camping. 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. On the other hand, Neuromarketing promises to overcome such constraints. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) and affective attitude (AA) from analyzing EEG signals. In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). 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. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. Moreover, negative AA signals shows more dispersion than positive AA signals. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences.

A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research

“…In the literature reviewed presently, machine learning was used primarily to predict consumer self-reported preference or buying behaviour [ 1 – 4 , 6 , 23 , 24 , 31 , 85 , 87 , 96 , 98 , 101 – 103 , 139 , 151 , 178 , 185 , 185 , 186 , 186 , 206 , 210 , 223 , 254 , 262 , 263 , 287 , 294 , 307 , 308 , 314 ]. However, it has yet to be demonstrated how machine learning can be used to improve advertisement or product designs.…”

“…Literature searches using the six journal databases used in the primary literature search were conducted using the search terms “consumer neuroscience”, “neuromarketing”, and “machine learning”. From this literature search, an additional eighteen studies were identified which used machine learning to predict consumer preference or purchase intention using EEG signals [ 4 , 6 , 23 , 24 , 87 , 96 , 185 , 185 , 186 , 186 , 206 , 210 , 237 , 263 , 287 , 291 , 294 , 307 , 308 , 313 ]. These additional studies have been included in the systematic review section.…”

“…Early studies primarily used multivariate analysis methods, such as logistic regressions, to predict preference ratings [ 24 , 85 , 101 , 139 , 237 , 303 ]. The subsequent use of machine-learning algorithms has been shown to improve the predictive accuracy of a model above the use of traditional logistic regressions [ 31 , 85 , 87 , 96 , 102 , 103 , 151 , 185 , 185 , 186 , 186 , 251 , 255 , 287 , 291 , 294 , 313 ]. Most studies reviewed employed the use of multiple machine-learning algorithms, allowing for the direct comparison of these methods [ 2 – 4 , 6 , 23 , 87 , 98 , 102 , 103 , 206 , 210 , 251 , 254 , 263 , 287 , 294 , 307 , 308 ] (see Table 4 ).…”

“…Across the reviewed literature, the most commonly used classification methods were DNN [ 2 , 3 , 6 , 313 ], KNN [ 2 , 3 , 6 , 98 , 102 , 103 , 206 , 210 , 263 , 308 ], SVM [ 1 – 3 , 6 , 87 , 98 , 102 , 103 , 185 , 185 , 186 , 186 , 210 , 263 , 291 , 307 , 308 , 314 ], RF [ 1 – 3 , 6 , 23 , 85 , 98 , 151 , 206 , 307 , 308 , 314 ], and regressions [ 1 , 24 , 85 , 102 , 103 , 138 , 237 , 251 ].…”

Neural mechanisms of expert persuasion on willingness to pay for sugar

“…Recently, electroencephalography (EEG) is used more and more to predict consumers’ preferences and choices (e.g., Hakim et al, 2021 ; Mashrur et al, 2022 ; Raiesdana and Mousakhani, 2022 ). EEG offers an opportunity to overcome the biases inherent in self-reports, such as dishonesty (Tourangeau and Smith, 1996 ), while at the same time, it allows to investigate the neural mechanisms underlying consumer behavior (Lin et al, 2018 ).…”