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An enhanced Coati Optimization Algorithm for global optimization and feature selection in EEG emotion recognition.

Essam H Houssein1, Asmaa Hammad1, Marwa M Emam1

  • 1Faculty of Computers and Information, Minia University, Minia, Egypt.

Computers in Biology and Medicine
|March 21, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces eCOA, an enhanced optimization algorithm for selecting Electroencephalography (EEG) features for emotion recognition. The new method improves classification accuracy, outperforming existing approaches in identifying emotional states from brain signals.

Keywords:
Coati Optimization AlgorithmEEG signalsEmotion recognitionFeature selectionMetaheuristicsMulti-layer perceptron neural network

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Area of Science:

  • Neuroscience
  • Computer Science
  • Artificial Intelligence

Background:

  • Emotion recognition using Electroencephalography (EEG) signals is crucial for applications in healthcare, education, and gaming.
  • A major challenge is the lack of standardized feature sets, leading to inefficient emotion classification.
  • High dimensionality of EEG data further complicates accurate emotion recognition.

Purpose of the Study:

  • To introduce an advanced optimization algorithm, eCOA, for selecting optimal EEG features for emotion recognition.
  • To address the limitations of the Coati Optimization Algorithm (COA), such as local optima and imbalanced exploitation.
  • To enhance the efficiency and accuracy of emotion classification from EEG signals.

Main Methods:

  • Developed eCOA by integrating the Coati Optimization Algorithm (COA) with the RUNge Kutta Optimizer (RUN), incorporating Scale Factor (SF) and Enhanced Solution Quality (ESQ) mechanisms.
  • Evaluated eCOA using the CEC'22 test suite and two EEG emotion recognition datasets (DEAP and DREAMER).
  • Applied eCOA for binary and multi-class emotion classification (valence, arousal, dominance) using a multi-layer perceptron neural network (MLPNN).

Main Results:

  • eCOA demonstrated superior search capabilities, convergence, and diversity compared to COA and seven other metaheuristic methods.
  • eCOA effectively performed feature selection, identifying optimal EEG features to maximize performance in emotion classification tasks.
  • Achieved high classification accuracies: 85.17% for arousal on DEAP and 95.21% for arousal on DREAMER, outperforming existing methods by significant margins.

Conclusions:

  • The proposed eCOA algorithm offers a powerful and efficient solution for EEG feature selection in emotion recognition.
  • eCOA significantly enhances the accuracy of emotion classification compared to current state-of-the-art approaches.
  • This advancement holds promise for more sophisticated and reliable brain-computer interfaces and emotion-aware systems.