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  3. Genetic Programming-based Feature Selection For Emotion Classification Using Eeg Signal.
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  3. Genetic Programming-based Feature Selection For Emotion Classification Using Eeg Signal.

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Genetic Programming-Based Feature Selection for Emotion Classification Using EEG Signal.

Aditi Sakalle1, Pradeep Tomar1, Harshit Bhardwaj1

  • 1CSE Department, University School of Information and Communication Technology, Gautam Buddha University, Greater Noida, India.

Journal of Healthcare Engineering
|March 18, 2022

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a genetic programming-based feature selection (FSGP) technique to improve emotion recognition from EEG data. The method effectively reduces features, enhancing accuracy for mental health diagnostics.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Mental Health Research

Background:

  • The COVID-19 pandemic and associated lockdowns have significantly impacted global mental health.
  • Emotion recognition is crucial for early diagnosis and effective treatment of mental health issues.
  • Artificial intelligence, particularly genetic programming (GP), shows promise in addressing complex research challenges.

Purpose of the Study:

  • To propose a novel genetic program-based feature selection (FSGP) technique for emotion recognition.
  • To identify and select relevant features from electroencephalogram (EEG) data for improved classification accuracy.
  • To enhance the efficacy of mental health diagnostics through advanced AI methods.

Main Methods:

  • Utilized a fourteen-channel electroencephalogram (EEG) device to acquire brain signal data.
  • Applied a genetic programming (GP) based feature selection (FSGP) technique to process 70 initial features.
  • Reduced the feature set by separating irrelevant and redundant data, selecting 32 key features.

    • Main Results:

    • The proposed FSGP model successfully selected 32 relevant features from the initial 70.
    • Achieved a classification accuracy of 85% for emotion recognition using the selected features.
    • Demonstrated superior performance compared to existing methods in prior research.

    Conclusions:

    • The FSGP technique is effective in optimizing feature selection for emotion recognition from EEG data.
    • This approach holds significant potential for improving the accuracy and efficiency of mental health diagnostics.
    • Further research in AI-driven emotion recognition can lead to better mental healthcare solutions.