A Comprehensive Overview of Automated Stress Recognition and Emotion Detection Systems using EEG signal

Abstract

Automated stress recognition and emotion detection systems utilizing electroencephalography (EEG) signals have emerged as promising avenues for understanding and enhancing mental health. This comprehensive overview delves into the intricate landscape of EEG-based technologies, elucidating their principles, methodologies, and applications in decoding stress and emotional states. Beginning with an exploration of EEG signal acquisition and processing techniques, this abstract navigates through the underlying neural correlates associated with stress and emotions, highlighting key EEG biomarkers. It further examines various machine learning and deep learning approaches employed for feature extraction and classification, emphasizing their efficacy in real-time detection and classification of stress and emotions. Moreover, this abstract elucidates the diverse array of applications spanning healthcare, human-computer interaction, and beyond, underscoring the transformative potential of EEG-based systems in fostering well-being and enhancing user experience. Additionally, it discusses the challenges and limitations inherent in EEG-based stress and emotion detection, ranging from signal artifacts to individual variability. Finally, it presents future directions and opportunities for research and development, advocating for interdisciplinary collaborations and advancements in technology to propel the field forward. In essence, this abstract serves as a comprehensive guide for researchers, practitioners, and enthusiasts alike, offering insights into the burgeoning domain of EEG-based automated stress recognition and emotion detection systems and paving the way for innovative solutions to promote mental health and enhance human-machine interactions.