Genetic Programming-Based Feature Selection for Emotion Classification Using EEG Signal.
Aditi SakallePradeep TomarHarshit BhardwajAsif IqbalManeesha SakalleArpit BhardwajWubshet IbrahimPublished in: Journal of healthcare engineering (2022)
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.
Keyphrases
- mental health
- machine learning
- artificial intelligence
- deep learning
- resting state
- big data
- genome wide
- functional connectivity
- autism spectrum disorder
- coronavirus disease
- copy number
- depressive symptoms
- working memory
- mental illness
- endothelial cells
- sars cov
- quality improvement
- dna methylation
- brain injury
- gene expression
- multiple sclerosis
- induced pluripotent stem cells
- borderline personality disorder
- subarachnoid hemorrhage
- combination therapy
- high density