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Identification of Emotion Using Electroencephalogram by Tunable Q-Factor Wavelet Transform and Binary Gray Wolf

Siyu Li1,2, Xiaotong Lyu1,2, Lei Zhao2,3

  • 1School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.

Frontiers in Computational Neuroscience
|September 27, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel electroencephalogram (EEG) based method for recognizing emotions. The approach achieves high accuracy in classifying emotional states using advanced signal processing and machine learning techniques.

Keywords:
EEGbinary gray wolf optimization algorithmemotion recognitionemotional brain-computer interfacetunable-Q wavelet transform

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Area of Science:

  • Human-Computer Interaction
  • Affective Computing
  • Biomedical Signal Processing

Background:

  • Emotional brain-computer interfaces (BCIs) are crucial for human-computer interaction and affective computing.
  • Accurate recognition of electroencephalogram (EEG) signals associated with emotions is a key challenge in this field.

Purpose of the Study:

  • To develop and evaluate a novel method for recognizing emotions from EEG signals.
  • To improve the performance of EEG-based emotion recognition systems.

Main Methods:

  • EEG data preprocessing and decomposition using tunable-Q wavelet transform.
  • Extraction of features including sample entropy, differential means, and Hjorth parameters.
  • Optimization of feature matrix using binary gray wolf optimization algorithm.
  • Classification of emotions using a support vector machine (SVM) classifier.

Main Results:

  • The proposed method achieved a maximum recognition accuracy of 90.48% for five types of emotion signals.
  • Sensitivity reached 70.25%, specificity was 82.01%, and the Kappa coefficient was 0.603.
  • The algorithm demonstrated robust performance indicators in recognizing multiple EEG emotion signals.

Conclusions:

  • The developed method shows significant potential for accurate EEG-based emotion recognition.
  • The approach offers improved performance compared to traditional methods in affective computing applications.