Eeg based smart emotion recognition using meta heuristic optimization and hybrid deep learning techniques

• Published in: Scientific reports 2024-12, Vol.14 (1), p.30251-24
• Main Authors: Karthiga, M, Suganya, E, Sountharrajan, S, Balusamy, Balamurugan, Selvarajan, Shitharth
• Format: Article
• Language: English
• Subjects: 639/166; 639/705; Accuracy; Algorithms; Artificial Bee colony; Biochips; Brain; Brain-Computer Interfaces; Computer applications; Convolutional neural network; Datasets; Deep Learning; EEG; Electroculogram; Electroencephalogram; Electroencephalography; Electroencephalography - methods; Electromyography; Emotions - physiology; Grey Wolf Optimizer; Heuristics; Humanities and Social Sciences; Humans; Hybrid learning methods; Implants; Male; multidisciplinary; Neural Networks, Computer; Problem solving; Science; Science (multidisciplinary); Signal Processing, Computer-Assisted; Theta rhythms
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-80448-5

In the domain of passive brain-computer interface applications, the identification of emotions is both essential and formidable. Significant research has recently been undertaken on emotion identification with electroencephalogram (EEG) data. The aim of this project is to develop a system that can analyse an individual’s EEG and differentiate among positive, neutral, and negative emotional states. The suggested methodology use Independent Component Analysis (ICA) to remove artefacts from Electromyogram (EMG) and Electrooculogram (EOG) in EEG channel recordings. Filtering techniques are employed to improve the quality of EEG data by segmenting it into alpha, beta, gamma, and theta frequency bands. Feature extraction is performed with a hybrid meta-heuristic optimisation technique, such as ABC-GWO. The Hybrid Artificial Bee Colony and Grey Wolf Optimiser are employed to extract optimised features from the selected dataset. Finally, comprehensive evaluations are conducted utilising DEAP and SEED, two publically accessible datasets. The CNN model attains an accuracy of approximately 97% on the SEED dataset and 98% on the DEAP dataset. The hybrid CNN-ABC-GWO model achieves an accuracy of approximately 99% on both datasets, with ABC-GWO employed for hyperparameter tuning and classification. The proposed model demonstrates an accuracy of around 99% on the SEED dataset and 100% on the DEAP dataset. The experimental findings are contrasted utilising a singular technique, a widely employed hybrid learning method, or the cutting-edge method; the proposed method enhances recognition performance.