Advancing Stress Diagnostics using EEG and Deep Learning Methods for Early Stress Identification: A Systematic Literature Survey

Stress is a growing global concern with significant psychological and physiological consequences, necessitating improved diagnostic methods for early detection and intervention. Electroencephalography (EEG) has emerged as a promising noninvasive tool for stress assessment, offering real-time insights into neurological responses. However, traditional methods struggle with the complexity and variability of EEG signals. This review systematically analyzes recent advancements in machine learning (ML) and deep learning (DL) applied to EEG-based stress detection. We assess various models, including Convolutional 1D (CONV1D), Bidirectional Long Short-Term Memory (BiLSTM), and Bidirectional Gated Recurrent Units (BiGRU), and their effectiveness in identifying stress patterns from EEG data. The study also explores the broader implications of EEG in diagnosing Alzheimer's disease and enabling brain-computer interfaces (BCIs), which enhance human-computer interactions, particularly for individuals with mobility impairments. Our findings suggest that integrating ML/DL with EEG can significantly enhance stress detection accuracy, paving the way for automated and scalable mental health diagnostics. By bridging the gap between neuroscience and artificial intelligence, this research contributes to the development of more precise, accessible, and preventive stress-monitoring solutions.