Framework for Human Emotion Detection and Analysis using Biomedical Signals

In our everyday lives, emotion and stress play a critical part in human interaction and communication. Apart from logical intelligence, emotional and stress intelligence are considered crucial components of human intelligence, including sensing, comprehending, and reacting to emotion and stress detection through sentiment analysis. However, modern systems for human-computer interaction lack emotional intelligence. By building computational models of stress and emotion analysis, affective brain-computer interfaces strive to bridge the communication gap between people and machines. Stress and emotion include human emotion, thinking, and action, and hence play a critical part in interpersonal communication. Its assessment automatically identifies various stress and emotional states by using physiological and non-physiological information gathered from people to facilitate communication and engagement with machines through sentiment analysis. Brainwave-Based Mood Estimation is an efficient approach for estimating stress and emotion. The technique demonstrates a significant role in treating human disorders, brain-computer interfaces for disabled people, and other research fields. This article will discuss numerous methodologies, strategies, and frameworks for electroencephalogram (EEG) estimates of human stress and mood.