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LEDPatNet19: Automated Emotion Recognition Model based on Nonlinear LED Pattern Feature Extraction Function using EEG

Turker Tuncer1, Sengul Dogan1, Abdulhamit Subasi2,3

  • 1Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey.

Cognitive Neurodynamics
|July 18, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces LEDPatNet19, a novel Electroencephalography (EEG) model for accurate emotion recognition. The framework combines hand-crafted features with deep learning, achieving high classification accuracies on established datasets.

Keywords:
Artificial intelligenceEmotion recognitionLed-patternMachine learningRFIChi2S-Box based feature generationTQWT

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Area of Science:

  • Neuroscience and Artificial Intelligence
  • Biomedical Signal Processing
  • Machine Learning for Emotion Recognition

Background:

  • Electroencephalography (EEG) signals are traditionally used for disease diagnosis.
  • EEG analysis offers potential applications in areas like emotion recognition and fatigue detection.
  • Existing methods may require further enhancement for robust and accurate emotion classification.

Purpose of the Study:

  • To develop a highly accurate emotion recognition framework utilizing EEG signals.
  • To introduce a novel model, LEDPatNet19, employing a hybrid approach of hand-crafted features and deep learning.
  • To explore the efficacy of a multilevel fused feature generation network for enhanced signal analysis.

Main Methods:

  • A multilevel fused feature generation network incorporating Tunable Q-factor Wavelet Transform (TQWT), statistical feature generation, and nonlinear textural feature generation (Led-Pattern).
  • Application of statistical moments and the novel Led-Pattern to 18 TQWT sub-bands and original EEG signals.
  • Feature selection using ReliefF and iterative Chi2 (RFIChi2) and model validation on the GAMEEMO and DREAMER EEG emotion datasets.

Main Results:

  • LEDPatNet19 achieved classification accuracies of 94.58% (arousal), 92.86% (dominance), and 94.44% (valence) on the DREAMER dataset.
  • The model attained a maximum classification accuracy of 99.29% on the GAMEEMO dataset.
  • The proposed hand-crafted learning network demonstrated significant success in emotion classification.

Conclusions:

  • The LEDPatNet19 framework effectively utilizes hand-crafted features and deep learning for accurate EEG-based emotion recognition.
  • The multilevel feature generation network, including TQWT and the novel Led-Pattern, significantly contributes to model performance.
  • The findings highlight the potential of LEDPatNet19 as a robust tool for emotion recognition from EEG data.