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EEG Channel Correlation Based Model for Emotion Recognition.

Md Rabiul Islam1, Md Milon Islam2, Md Mustafizur Rahman3

  • 1Electrical and Electronic Engineering, Bangladesh Army University of Engineering & Technology, Natore, 6431, Bangladesh; Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh.

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|August 20, 2021
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Summary

This study introduces a deep learning model using Convolutional Neural Networks (CNNs) for emotion recognition from Electroencephalogram (EEG) signals. The model achieves high accuracy by converting EEG data into images, improving Human-Computer Interaction (HCI).

Keywords:
ComplexityConvolutional neural networkEEGEmotionFeature extractionPearson's correlation coefficient

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Area of Science:

  • Artificial Intelligence
  • Neuroscience
  • Human-Computer Interaction

Background:

  • Emotion recognition is crucial for advancing Human-Computer Interaction (HCI).
  • Electroencephalogram (EEG) signals offer a promising but challenging avenue for emotion detection due to low amplitude variations.
  • Traditional feature extraction for EEG-based emotion recognition is labor-intensive.

Purpose of the Study:

  • To develop a deep learning model for automated emotion recognition from EEG signals.
  • To overcome the challenges of manual feature extraction in EEG analysis.
  • To enhance the accuracy and efficiency of emotion recognition systems.

Main Methods:

  • A Convolutional Neural Network (CNN) model was employed for deep learning-based emotion recognition.
  • One-dimensional EEG data were transformed into Pearson's Correlation Coefficient (PCC) featured images representing channel correlations.
  • The upper triangular portion of PCC images was utilized to reduce computational complexity without compromising accuracy.

Main Results:

  • The proposed CNN model successfully recognized emotions from EEG signals.
  • Maximum accuracies of 78.22% for valence and 74.92% for arousal were achieved on the DEAP dataset.
  • Utilizing the upper triangular part of PCC images proved effective in reducing model size and computational load.

Conclusions:

  • Deep learning, specifically CNNs, offers an effective solution for automated emotion recognition from EEG data.
  • The PCC-featured image conversion method, optimized with the upper triangular portion, enhances efficiency.
  • This approach holds significant potential for improving affective computing and HCI applications.