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Emotion recognition while applying cosmetic cream using deep learning from EEG data; cross-subject analysis.

Jieun Kim1,2, Dong-Uk Hwang1, Edwin J Son1

  • 1Division of Fundamental Research on Public Agenda, National Institute for Mathematical Sciences, Daejeon, South Korea.

Plos One
|November 10, 2022
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Summary
This summary is machine-generated.

This study used deep learning and electroencephalography (EEG) to recognize emotions associated with cosmetic creams. Multi-band EEG features improved emotion recognition accuracy for cosmetic preferences.

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Area of Science:

  • Neuroscience
  • Cosmetic Science
  • Artificial Intelligence

Background:

  • Consumer preference for cosmetics is subjective and difficult to quantify.
  • Electroencephalography (EEG) offers a potential objective measure of emotional response.
  • Deep learning models can analyze complex neural data for pattern recognition.

Purpose of the Study:

  • To develop and evaluate a deep learning-based method for recognizing emotions (like/dislike) towards cosmetic creams using EEG data.
  • To investigate the effectiveness of different EEG frequency bands and feature extraction methods for emotion recognition in cosmetic applications.

Main Methods:

  • Collected EEG data from subjects applying four different cosmetic creams.
  • Extracted frequency features from alpha, beta, low gamma, and high gamma bands.
  • Developed and compared seven Convolutional Neural Network (CNN) models, including multi-band and single-band inputs.
  • Evaluated models using Leave-One-Subject-Out Cross-Validation.

Main Results:

  • The highest average accuracy for classifying like/dislike emotions was 75.4% using a stacked CNN with a four-band merged input.
  • Models utilizing multi-band EEG features demonstrated superior classification performance compared to single-band features.
  • This research represents the first application of CNN-based deep learning with EEG for cosmetic preference evaluation.

Conclusions:

  • Deep learning models, particularly CNNs utilizing multi-band EEG features, can effectively recognize user emotions towards cosmetic products.
  • Objective emotion recognition using EEG could inform cosmetic product development and marketing.
  • Further research can explore more sophisticated deep learning architectures and feature combinations for enhanced accuracy.