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Eeg based smart emotion recognition using meta heuristic optimization and hybrid deep learning techniques.

M Karthiga1, E Suganya2, S Sountharrajan3

  • 1Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Tamilnadu, India.

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|December 5, 2024
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Summary

This study introduces an advanced brain-computer interface (BCI) for emotion recognition using electroencephalogram (EEG) data. The novel hybrid CNN-ABC-GWO model achieves superior accuracy in classifying emotional states.

Keywords:
Artificial Bee colonyConvolutional neural networkElectroculogramElectroencephalogramGrey Wolf OptimizerHybrid learning methods

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Area of Science:

  • Neuroscience and Artificial Intelligence
  • Brain-Computer Interface (BCI) applications
  • Emotion Recognition using physiological signals

Background:

  • Emotion identification from electroencephalogram (EEG) data is crucial for passive brain-computer interface (BCI) applications.
  • Accurate analysis of EEG signals is essential for differentiating emotional states (positive, neutral, negative).
  • Existing methods require refinement for improved emotion recognition performance.

Purpose of the Study:

  • To develop a robust system for analyzing EEG data to classify human emotional states.
  • To enhance EEG signal quality by removing artifacts and optimizing feature extraction.
  • To evaluate the performance of a novel hybrid model for emotion recognition.

Main Methods:

  • EEG data preprocessing included Independent Component Analysis (ICA) for artifact removal (Electromyogram - EMG, Electrooculogram - EOG).
  • Signal filtering segmented EEG into alpha, beta, gamma, and theta frequency bands.
  • Feature extraction utilized a hybrid meta-heuristic optimization technique (Artificial Bee Colony - ABC and Grey Wolf Optimizer - GWO), followed by Convolutional Neural Network (CNN) classification with hyperparameter tuning.

Main Results:

  • The hybrid CNN-ABC-GWO model achieved approximately 99% accuracy on both the SEED and DEAP datasets.
  • The proposed model demonstrated superior performance compared to singular techniques and other hybrid learning methods.
  • The system achieved 100% accuracy on the DEAP dataset, indicating high efficacy in emotion recognition.

Conclusions:

  • The developed hybrid CNN-ABC-GWO model significantly enhances emotion recognition accuracy from EEG data.
  • This approach offers a promising advancement for passive BCI applications requiring reliable emotional state identification.
  • The methodology provides a powerful tool for analyzing complex EEG patterns related to human emotions.