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

Journal: Journal of healthcare engineering
Published Date: Mar 8, 2022

Abstract

The COVID-19 has resulted in one of the world's most significant worldwide lock-downs, affecting human mental health. Therefore, emotion recognition is becoming one of the essential research areas among various world researchers. Treatment that is efficacious and diagnosed early for negative emotions is the only way to save people from mental health problems. Genetic programming, a very important research area of artificial intelligence, proves its potential in almost every field. Therefore, in this study, a genetic program-based feature selection (FSGP) technique is proposed. A fourteen-channel EEG device gives 70 features for the input brain signal; with the help of GP, all the irrelevant and redundant features are separated, and 32 relevant features are selected. The proposed model achieves a classification accuracy of 85% that outmatches other prior works.

Authors

  • Aditi Sakalle
    CSE Department, Gautam Buddha University, Greater Noida, India.
  • Pradeep Tomar
    CSE Department, Gautam Buddha University, Greater Noida, India.
  • Harshit Bhardwaj
    CSE Department, Gautam Buddha University, Greater Noida, India.
  • Asif Iqbal
  • Maneesha Sakalle
    Department of Mathematics, SNGPG, College Khandwa, Khandwa, India.
  • Arpit Bhardwaj
    Department of Computer Science and Engineering, BML Munjal University, Gurugram, India.
  • Wubshet Ibrahim
    Department of Mathematics, Ambo University, Ambo, Ethiopia.

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