Intelligent optimal feature selection-based hybrid variational autoencoder and block recurrent transformer network for accurate emotion recognition model using EEG signals

In the context of emotion recognition, Artificial Intelligence technology has demonstrated several functions in people's lives. Computing research is now focused on Electroencephalogram (EEG) signals to identify emotional states. The connection and interaction between multichannel EEG signals give important information about emotional states. However, most existing emotion identification techniques perform poorly in practical applications by preventing their advancement. The main objective of this paper is to design an efficient model for emotion recognition based on deep learning technology by EEG signals. The proposed model for emotion recognition collects the EEG signals from the standard benchmark datasets. Then, the signal decomposition is performed using the Tunable Q-factor Wavelet Transform with the collected EEG signals. The decomposed signals are taken for the optimal feature selection phase, where the significant features of the emotion are selected through the hybrid optimization algorithm named Aquila Fireworks Optimization Algorithm (AFOA). Finally, the EEG emotion classification is performed using Hybrid Variational Autoencoder and Block Recurrent Transformer Network. The tuning of the parameter is made through the same AFOA to improve the efficiency of classification. The experimental analysis is conducted to analyze the efficiency of the recommended emotion recognition model with the comparison over the traditional techniques.