Detection of Drowsiness using EEG Probes Sensory Logic Signals Activeness Topology

Abstract

Drowsiness detection has received a great deal of attention, and there are numerous EEG-based techniques for it. The signals are initially filtered, conditioned, and features are abstracted in this method, which focuses on post-processing, to assess the driver's drowsiness status. This method is frequently used, especially when the procedure's output yields odd results, as indicated in the literature. When a subject is in a dynamic position, such as driving when movement cannot be prevented or minimized, EEG data is difficult to get, and EEG signals are prone to artifacts such as muscle and head movement, among other things. Filtering is a software method for removing physical artifacts throughout the pre-and post-processing stages. This technique will take some time to develop and will have an impact on the overall detection time of the system. Algorithms for logic determination are used to determine whether the EEG probe's logic activity is active or inactive and to interpret it as drowsy. Data was collected from five healthy people aged 20 to 27 to put this technique to the test. Participants were instructed to continue driving while EEG data were collected and compared to sensor probe output to determine which wavelength best reflected their weariness. Sensory Logic monitors brain activity by measuring the strength of electrons gathered in a particular cortical location. When the two detection procedures are compared, the PSD approach has higher sensitivity and accuracy for detecting drowsiness, but the Sensor Boolean output falls short in the detection spectrum, as proven later.