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A novel consciousness emotion recognition method using ERP components and MMSE.

Xiangwei Zheng1,2, Min Zhang1,2, Tiantian Li3

  • 1School of Information Science and Engineering, Shandong Normal University, Jinan, People's Republic of China.

Journal of Neural Engineering
|February 26, 2021
PubMed
Summary

This study introduces a new method for emotion recognition using electroencephalogram (EEG) signals by fusing event-related potential (ERP) components with modified multi-scale sample entropy (MMSE). This approach significantly improves the accuracy of identifying consciousness and unconsciousness emotions.

Keywords:
ERP componentsconsciousness emotion recognitionshapeletvariational mode decompositionwavelet packet decomposition

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Area of Science:

  • Neuroscience
  • Computational Psychology
  • Signal Processing

Background:

  • Electroencephalogram (EEG) based emotion recognition often suffers from low accuracy due to the complexity of EEG signals.
  • Traditional methods primarily use time and frequency domain features, neglecting valuable event-related potential (ERP) components.
  • Manual identification of ERP components is time-consuming and requires expert knowledge.

Purpose of the Study:

  • To develop an automated method for emotion recognition by integrating ERP components and nonlinear features from EEG signals.
  • To enhance the accuracy of classifying consciousness and unconsciousness emotions.
  • To provide a novel approach for analyzing nonlinear time series in emotion recognition.

Main Methods:

  • Automated identification and extraction of ERP components (N200, P300, N300) using the shapelet technique.
  • Variational mode decomposition and wavelet packet decomposition to process EEG signals and extract modified multi-scale sample entropy (MMSE) features.
  • Fusion of extracted ERP components and MMSE features into a new vector for classification using a random forest model.

Main Results:

  • The proposed method achieved high average classification accuracies: 94.42% for happiness, 94.88% for horror, and 94.95% for anger.
  • Demonstrated the effectiveness of fusing ERP components with nonlinear features for emotion recognition.
  • Successfully classified consciousness and unconsciousness emotions with improved accuracy.

Conclusions:

  • The fusion of event-related potential (ERP) components and modified multi-scale sample entropy (MMSE) offers a more effective approach for consciousness and unconsciousness emotion recognition.
  • This study presents a novel, automated method for EEG-based emotion recognition, reducing reliance on manual analysis.
  • The findings open new research avenues in nonlinear time series analysis for affective computing.