Real-Time EEG-Based Drowsiness Detection Using Deep Learning Algorithms

Driver drowsiness remains a critical contributor to road accidents globally, necessitating reliable and timely detection systems to enhance safety. Electroencephalography (EEG), with its capacity to capture direct neural correlates of cognitive states, has emerged as a leading modality for real-time drowsiness monitoring. This paper reviews recent advancements in EEG-based drowsiness detection systems, emphasizing innovations in signal processing, machine learning, and edge computing that enable low-latency, high-accuracy analysis. Current trends highlight the adoption of deep learning architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to decode drowsiness-related EEG patterns, alongside hardware advancements that support portable, non-invasive wearable devices. Despite progress, challenges persist in addressing motion artifacts, inter-subject variability, and the need for robust generalization across diverse populations. Emerging opportunities lie in hybrid systems integrating EEG with complementary sensors (e.g., eye-tracking, heart rate monitors) to improve reliability, as well as the development of adaptive algorithms for personalized drowsiness prediction. Furthermore, the rise of edge AI and energy-efficient embedded systems offers promising pathways for deploying real-time detection frameworks in resource-constrained environments. This analysis underscores the transformative potential of EEG-driven drowsiness detection while advocating for interdisciplinary collaboration to address technical, ethical, and practical barriers to widespread implementation.