Enhanced Spatial-Temporal Analysis for EEG-Based Microsleep Detection: Integrating Kalman Filtering with Voronoi Tessellation and Adaptive Coverage Control

Detecting microsleep in real time is crucial to facilitating the transition from semi-autonomous systems to completely autonomous driving technologies. Integrating sophisticated detection algorithms with vehicle control systems enables the provision of prompt corrective measures, such as driver alerts or temporary vehicle control. Minimizing the probability of accidents caused by driver fatigue not only improves the safety of users, but also promotes the overall security of the road network. By integrating Kalman filtering, Voronoi tessellation, and adaptive coverage control algorithms, the study seeks to find a feasible and sophisticated methodology to enhance the spatial and temporal resolution of EEG data analysis, leading to more robust and reliable detection of microsleep episodes. The paper presents a framework as a significant advancement in sleep technology, offering a new method to diagnose and understand microsleeps, characteristics, and patterns of brain activity and sleep disorders.