A Comparative Study of EEG-Based Stress Detection Using Deep Learning and Yoga Intervention Strategies

Abstract

In today’s fast-paced world, chronic stress has become a major health concern, often leading to issues such as anxiety, high blood pressure, and reduced cognitive performance. This study explores two complementary approaches to managing stress — the therapeutic benefits of yoga and the use of electroencephalography (EEG) combined with machine learning for stress detection. Yoga, a holistic practice that blends physical postures, breathing techniques, and mindfulness, has been shown to enhance emotional balance and reduce the body’s physiological responses to stress. Concurrently, recent developments in EEG-based stress analysis have used deep learning models, such as hybrid architectures, long short-term memory (LSTM) networks, and convolution neural networks (CNNs), to accurately classify emotional stress states. Studies that have been published show encouraging outcomes in real-time detection and classification performance, especially when hybrid CNN models and multimodal sensor data are used. In order to enable proactive stress management solutions, future research is focused on creating customized stress prediction frameworks, boosting real-time detection systems, and improving the interpretability of AI-driven models.