An intelligent neuromarketing system for predicting consumers’ future choice from electroencephalography signals

Mashrur, Fazla Rabbi; Rahman, Khandoker Mahmudur; Miya, Mohammad Tohidul Islam; Vaidyanathan, Ravi; Anwar, Syed Ferhat; Sarker, Farhana; Mamun, Khondaker A.

doi:10.1016/j.physbeh.2022.113847

Cited by 34 publications

(27 citation statements)

References 54 publications

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“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Methodsmentioning

confidence: 99%

“…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 ).…”

Section: Systematic Reviewmentioning

confidence: 99%

“…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 ].…”

Section: Systematic Reviewmentioning

confidence: 99%

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

et al. 2022

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Introduction The present paper discusses the findings of a systematic review of EEG measures in neuromarketing, identifying which EEG measures are the most robust predictor of customer preference in neuromarketing. The review investigated which TF effect (e.g., theta-band power), and ERP component (e.g., N400) was most consistently reflective of self-reported preference. Machine-learning prediction also investigated, along with the use of EEG when combined with physiological measures such as eye-tracking. Methods Search terms ‘neuromarketing’ and ‘consumer neuroscience’ identified papers that used EEG measures. Publications were excluded if they were primarily written in a language other than English or were not published as journal articles (e.g., book chapters). 174 papers were included in the present review. Results Frontal alpha asymmetry (FAA) was the most reliable TF signal of preference and was able to differentiate positive from negative consumer responses. Similarly, the late positive potential (LPP) was the most reliable ERP component, reflecting conscious emotional evaluation of products and advertising. However, there was limited consistency across papers, with each measure showing mixed results when related to preference and purchase behaviour. Conclusions and implications FAA and the LPP were the most consistent markers of emotional responses to marketing stimuli, consumer preference and purchase intention. Predictive accuracy of FAA and the LPP was greatly improved through the use of machine-learning prediction, especially when combined with eye-tracking or facial expression analyses.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Systematic Reviewmentioning

confidence: 99%

Section: Systematic Reviewmentioning

confidence: 99%

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

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“…Indeed, the biometrics and neuroscience field has been the focus of studies by many researchers who have achieved significant results. A number of neuroscience studies have analyzed the detection and recognition of human arousal [ 34 ], valence [ 35 , 36 ], affective attitudes [ 36 , 37 ], emotional [ 38 , 39 , 40 , 41 ], and physiological [ 42 ] states (AFFECT) by capturing human signals.…”

Section: Introductionmentioning

confidence: 99%

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Kaklauskas

Abraham

Ubarte

et al. 2022

Sensors

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Affective, emotional, and physiological states (AFFECT) detection and recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim is to review publications on how techniques that use brain and biometric sensors can be used for AFFECT recognition, consolidate the findings, provide a rationale for the current methods, compare the effectiveness of existing methods, and quantify how likely they are to address the issues/challenges in the field. In efforts to achieve the key goals of Society 5.0, Industry 5.0, and human-centered design better, the recognition of emotional, affective, and physiological states is progressively becoming an important matter and offers tremendous growth of knowledge and progress in these and other related fields. In this research, a review of AFFECT recognition brain and biometric sensors, methods, and applications was performed, based on Plutchik’s wheel of emotions. Due to the immense variety of existing sensors and sensing systems, this study aimed to provide an analysis of the available sensors that can be used to define human AFFECT, and to classify them based on the type of sensing area and their efficiency in real implementations. Based on statistical and multiple criteria analysis across 169 nations, our outcomes introduce a connection between a nation’s success, its number of Web of Science articles published, and its frequency of citation on AFFECT recognition. The principal conclusions present how this research contributes to the big picture in the field under analysis and explore forthcoming study trends.

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Intelligent neuromarketing framework for consumers' preference prediction from electroencephalography signals and eye tracking

Mashrur,

Rahman,

Miya

et al. 2023

J of Consumer Behaviour

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Neuromarketing uses brain‐computer interface technology to understand customer preferences in response to marketing stimuli. Every year, marketing professionals spend over $750 Billion (US dollars) on traditional marketing, which is usually behavioral and subjective, focusing on self‐reports acquired via questionnaires, focus groups, and depth interviews. Neuromarketing, on the other hand, promises to overcome such limitations. This work proposes a machine learning framework that incorporates multiple components (endorsement, offer, and slogan) in real advertisement to predict consumer preference from electroencephalography (EEG) signals. In addition, we also use eye‐tracking data to visualize consumer viewing patterns according to both advertisement type and preference. EEG signals are collected from 22 healthy volunteers while viewing the real ads as stimuli. After preprocessing the signals, three‐domain features are extracted (time, frequency, and time‐frequency). Then, using wrapper‐based approaches we choose best features which are later classified into strong and weak preferences using the support vector machine. The experimental results demonstrate the best performance using all the frontal channels with an accuracy of 96.97%, sensitivity of 96.30%, and specificity of 97.44%. Additionally, eye tracking data reveals that subjects substantially prefer an ad, when they first glance at the endorsement. In addition, people tend to blink their eyes less frequently while viewing ads with endorsements and strongly prefer these commercials too. Additionally, our work lays the door for deploying such a neuromarketing framework in a real‐world context by employing consumer‐grade EEG equipment. Therefore, it is evident that neuromarketing technology may assist brands and companies in accurately predicting future customer preferences.

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An intelligent neuromarketing system for predicting consumers’ future choice from electroencephalography signals

Cited by 34 publications

References 54 publications

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

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

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Intelligent neuromarketing framework for consumers' preference prediction from electroencephalography signals and eye tracking

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