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Multimodal consumer choice prediction using EEG signals and eye tracking.

Syed Muhammad Usman1, Shehzad Khalid2, Aimen Tanveer3

  • 1Department of Computer Science, Bahria School of Engineering and Applied Science, Bahria University, Islamabad, Pakistan.

Frontiers in Computational Neuroscience
|January 23, 2025
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Summary

This study introduces a new neuromarketing approach combining electroencephalogram (EEG) and eye tracking (ET) to predict consumer choices. The multimodal model achieved 84.01% accuracy, outperforming existing methods in understanding consumer behavior.

Keywords:
CNN-LSTMEEGeye trackingmultimodalneuromarketing

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Area of Science:

  • Neuroscience
  • Marketing Science
  • Computer Science

Background:

  • Neuromarketing enhances traditional marketing by analyzing consumer brain activity and emotions.
  • Electroencephalogram (EEG) is commonly used, but Eye Tracking (ET) remains underexplored in this field.
  • Predicting consumer choices requires advanced analytical methods integrating multiple data sources.

Purpose of the Study:

  • To develop and validate a novel multimodal approach for predicting consumer choices.
  • To integrate electroencephalogram (EEG) and eye tracking (ET) data for enhanced predictive accuracy.
  • To address the gap in utilizing eye tracking data within neuromarketing research.

Main Methods:

  • EEG and ET data were preprocessed, including noise reduction (bandpass filter, ASR, FORCE) and artifact handling (SMOTE).
  • Feature extraction involved both handcrafted (statistical, wavelet, fixations, saccades) and automated methods (CNN-LSTM, LeNet-5).
  • A meta-learner ensemble classifier (Random Forest, XGBoost, Gradient Boosting) was employed for buy/not buy classification.

Main Results:

  • The multimodal approach achieved 84.01% accuracy in predicting consumer choices.
  • The model demonstrated 83% precision in identifying positive consumer preferences.
  • Performance was evaluated using accuracy, precision, recall, and F1 score, showing superior results.

Conclusions:

  • Integrating EEG and ET data offers a powerful method for predicting consumer choices in neuromarketing.
  • The proposed multimodal approach significantly improves the accuracy and precision of consumer behavior prediction.
  • This research highlights the potential of combining neurophysiological and behavioral data for more effective marketing strategies.