Intelligent analysis of EEG signals to assess consumer decisions: A Study on Neuromarketing

Phutela, Nikunj; Abhilash, P; Sreevathsan, Kaushik; Krupa, Beata

doi:10.1109/incet54531.2022.9824698

Cited by 2 publications

(2 citation statements)

References 20 publications

(42 reference statements)

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“…The study found that participants who chose the electric car had higher power spectral density, specific spatiotemporal dynamical differences, and the ability to integrate information over long periods, suggesting that intrinsically extracted measures may predict consumers' buying behavior. Another study (Phutela et al, 2022) analyzed EEG recordings of 13 subjects using the Neurosky Mindwave device. The results showed that the linear classifier performed best in Subject Dependent analysis, while non‐linear classifiers had better accuracy in gender‐based analysis.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

“…Another study (Phutela et al, 2022) analyzed EEG recordings of 13 subjects using the Neurosky Mindwave device. The results showed that the linear classifier performed best in Subject Dependent analysis, while non-linear classifiers had better accuracy in gender-based analysis.…”

mentioning

confidence: 99%

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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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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Intelligent analysis of EEG signals to assess consumer decisions: A Study on Neuromarketing

Cited by 2 publications

References 20 publications

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

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

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

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