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

Aditi Sakalle | Zendy; Pradeep Tomar | Zendy; Harshit Bhardwaj | Zendy; Asif Iqbal | Zendy; Maneesha Sakalle | Zendy; Arpit Bhardwaj | Zendy; Wubshet Ibrahim | Zendy

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

Author(s) -

Aditi Sakalle,

Pradeep Tomar,

Harshit Bhardwaj,

Asif Iqbal,

Maneesha Sakalle,

Arpit Bhardwaj,

Wubshet Ibrahim

Publication year - 2022

Publication title -

journal of healthcare engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.509

H-Index - 29

eISSN - 2040-2309

pISSN - 2040-2295

DOI - 10.1155/2022/8362091

Subject(s) - genetic programming , computer science , feature selection , artificial intelligence , feature (linguistics) , field (mathematics) , selection (genetic algorithm) , electroencephalography , mental health , signal (programming language) , machine learning , genetic algorithm , psychology , psychiatry , mathematics , philosophy , linguistics , pure mathematics , programming language

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.

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