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Machine learning-based viewers' preference prediction on social awareness advertisements using EEG.

Farhan Ishtiaque1, Mohammad Tohidul Islam Miya2, Fazla Rabbi Mashrur3

  • 1AIMS Lab, IIRIC, UIU, Dhaka, Bangladesh.

Frontiers in Human Neuroscience
|June 30, 2025
PubMed
Summary
This summary is machine-generated.

Neuromarketing accurately predicts consumer ad preference using EEG data. The engagement index, derived from brainwave activity, is a key indicator of viewer response to dynamic advertisements.

Keywords:
EEGconsumer neuroscienceconsumer preference predictionmachine learningneuromarketing

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Area of Science:

  • Neuromarketing
  • Consumer Neuroscience
  • Machine Learning

Background:

  • Neuromarketing effectively predicts consumer preference for static ads and e-commerce products.
  • Developing neuromarketing systems for dynamic advertisements requires further research.
  • This study focuses on predicting consumer preference for awareness advertisements using neural clues.

Purpose of the Study:

  • To predict consumer preference for dynamic awareness advertisements.
  • To explore neural indicators for evaluating advertisement effectiveness.
  • To advance neuromarketing techniques for dynamic ad analysis.

Main Methods:

  • Utilized 8 awareness advertisements across 4 topics, employing 'shock' and 'comic' storytelling.
  • Collected a 14-channel electroencephalography (EEG) dataset from 20 participants.
  • Applied machine learning for binary classification of viewer preferences and analyzed engagement index and alpha activity.

Main Results:

  • Achieved a highest average accuracy of 72% using a leave-one-ad-out cross-validation method.
  • The engagement index (beta/alpha + theta or beta/alpha) significantly correlates with self-reported ad ratings.
  • The developed machine learning model surpasses current state-of-the-art accuracy.

Conclusions:

  • This study demonstrates the efficacy of neuromarketing for dynamic awareness advertisements.
  • The engagement index serves as a crucial neural marker for ad preference prediction.
  • Utilizing bias-free awareness ads provides novel insights into advertisement design and storytelling impact.