Published Paper Details:

Automated systems for stress recognition and emotion detection leveraging electroencephalography (EEG) signals have emerged as cutting-edge tools in advancing mental health research and applications. This review offers an in-depth exploration of EEG-based technologies, shedding light on their foundational principles, methodologies, and practical implementations for interpreting stress and emotional states. Starting with an examination of EEG signal acquisition and preprocessing techniques, it delves into the neural dynamics linked to stress and emotions, with a focus on identifying critical EEG biomarkers. The discussion extends to include various machine learning and deep learning strategies for feature extraction and classification, highlighting their effectiveness in real-time detection scenarios. Furthermore, the review underscores the diverse applications of these technologies, ranging from healthcare and therapeutic interventions to human-computer interaction, demonstrating their potential to improve well-being and enhance user experiences. Key challenges, including signal artifacts, individual variability, and system reliability, are also critically examined. The review concludes by exploring future research directions, advocating for interdisciplinary collaborations and technological innovations to overcome existing limitations and drive progress in the field. This comprehensive overview serves as a valuable resource for researchers, developers, and practitioners, offering insights into the rapidly evolving domain of EEG-based stress and emotion detection systems and inspiring advancements to support mental health and human-machine synergy