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Feedback Artificial Shuffled Shepherd Optimization-Based Deep Maxout Network for Human Emotion Recognition Using EEG

K S Bhanumathi1, D Jayadevappa1, Satish Tunga2

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This summary is machine-generated.

This study introduces a novel Feedback Artificial Shuffled Shepherd Optimization (FASSO) approach for emotion recognition using electroencephalogram (EEG) signals. The FASSO-based Deep Maxout Network (DMN) achieves high accuracy in identifying human emotions from EEG data.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Signal Processing

Background:

  • Emotion recognition is crucial for human interaction and self-awareness.
  • Recognizing emotions from electroencephalogram (EEG) signals remains challenging due to emotional complexity.
  • Existing methods require effective algorithms for accurate EEG-based emotion detection.

Purpose of the Study:

  • To develop an effective human emotion recognition approach using EEG signals.
  • To propose a novel Feedback Artificial Shuffled Shepherd Optimization (FASSO) algorithm.
  • To integrate FASSO with a Deep Maxout Network (DMN) for enhanced emotion recognition.

Main Methods:

  • EEG signal preprocessing using a median filter to remove noise.
  • Extraction of various features including DWT, spectral flatness, and logarithmic band power.
  • Training a Deep Maxout Network (DMN) using the proposed FASSO optimization technique.

Main Results:

  • The FASSO-DMN model demonstrated efficient performance in emotion recognition.
  • Maximal accuracy, specificity, and sensitivity values achieved were 0.889, 0.89, and 0.886, respectively.
  • Data augmentation further improved the capabilities of the DMN for emotion recognition.

Conclusions:

  • The proposed FASSO-based DMN is an effective method for EEG emotion recognition.
  • The integration of FASSO with DMN offers significant improvements in accuracy and reliability.
  • This approach holds promise for advancing the field of affective computing and human-computer interaction.