Sentiment Analysis-Based Automatic Stress and Emotion Recognition using Weighted Fused Fusion-Based Cascaded DTCN with Attention Mechanism from EEG Signal

Abstract

When loaded with difficulties in fulfilling daily requirements, a lot of people in today’s world experience an emotional pressure known as stress. Stress that lasts for a short duration of time has more advantages as they are good for mental health. But, the persistence of stress for a long duration of time may lead to serious health impacts in individuals, such as high blood pressure, cardiovascular disease, stroke and so on. Long-term stress, if unidentified and not treated, may also result in personality disorder, depression and anxiety. The initial detection of stress has become more important to prevent the health issues that arise due to stress. Detection of stress based on brain signals for analysing the emotion in humans leads to accurate detection outcomes. Using EEG-based detection systems and disease, disability and disorders can be identified from the brain by utilising the brain waves. Sentiment Analysis (SA) is helpful in identifying the emotions and mental stress in the human brain. So, a system to accurately and precisely detect depression in human based on their emotion through the utilisation of SA is of high necessity. The development of a reliable and precise Emotion and Stress Recognition (ESR) system in order to detect depression in real-time using deep learning techniques with the aid of Electroencephalography (EEG) signal-based SA is carried out in this paper. The essentials needed for performing stress and emotion detection are gathered initially from benchmark databases. Next, the pre-processing procedures, like the removal of artifacts from the gathered EEG signal, are carried out on the implemented model. The extraction of the spectral attributes is carried out from the pre-processed. The extracted spectral features are considered the first set of features. Then, with the aid of a Conditional Variational Autoencoder (CVA), the deep features are extracted from the pre-processed signals forming a second set of features. The weights are optimised using the Adaptive Egret Swarm Optimisation Algorithm (AESOA) so that the weighted fused features are obtained from these two sets of extracted features. Then, a Cascaded Deep Temporal Convolution Network with Attention Mechanism (CDTCN-AM) is used to recognise stress and emotion. The validation of the results from the developed stress and emotion recognition approach is carried out against traditional models in order to showcase the effectiveness of the suggested approach.