Journal article
Identifying periods of drowsy driving using EEG
Annals of advances in automotive medicine, Vol.57, pp.99-108
2013
PMCID: PMC3861841
PMID: 24406950
Abstract
Drowsy driving is a significant contributor to death and injury crashes on our nation's highways. Predictive neurophysiologic/physiologic solutions to reduce these incidences have been proposed and developed. EEG based metrics were found to be promising in initial studies, but remain controversial in their efficacy, primarily due to failures to develop replication studies within the simulation settings used for development, and real-world validation. This analysis sought to address these short comings by assessing the utility of the B-Alert algorithms, in a replication study of driving and drowsiness. Data were collected on the National Advanced Driving Simulator from 72 volunteer drivers exposed to three types of roadways at three times of day representing different levels of drowsiness. EEG metrics, collected using the B-Alert X10 Wireless Headset were evaluated to determine their utility in future predictive studies. The replication of the B-Alert algorithms was a secondary focus for this analysis, resulting in highly variable start times within each time of day segment, leading to EEG data being confounded by the diurnal variations that occur in the basal EEG signal. Regardless of this limitation, the analysis revealed promising outcomes. The EEG based algorithms for sleep onset, drowsiness, as well as fatigue related power spectral bandwidths (i.e. lateral central, and parietal alpha) varied with time of day of the drives. Interestingly, EEG metrics of cognitive workload were also sensative to the terrain of the drives. The replicaiton of the B-Alert algorithms were a secondary focuse in the study design, Taken together, these data indicate great potential of carefully designed studies to utilize neurophysiologic metrics to identify time of day and task and road conditions that may be at greatest risk during fatigued/drowsy periods.
Details
- Title: Subtitle
- Identifying periods of drowsy driving using EEG
- Creators
- Timothy Brown - National Advanced Driving Simulator, Center for Computer Aided Design, The University of Iowa, Iowa City, IowaRobin Johnson - Advanced Brain MonitoringGary Milavetz - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Annals of advances in automotive medicine, Vol.57, pp.99-108
- PMID
- 24406950
- PMCID
- PMC3861841
- NLM abbreviation
- Ann Adv Automot Med
- ISSN
- 1943-2461
- Language
- English
- Date published
- 2013
- Academic Unit
- Pharmacy; Pharmaceutical Sciences and Experimental Therapeutics; Pharmacy Practice and Science; Injury Prevention Research Center
- Record Identifier
- 9984366013802771
Metrics
15 Record Views