Identifying periods of drowsy driving using EEG (original) (raw)
Related papers
2008
The aim of this study was to identify a useful measure to estimate an arousal level of drivers, to apply the result to develop ITS (Intelligent Transportation System) that can warn drivers of their low arousal state and to prevent driving under low arousal level from occurring and contribute to the reduction of traffic accidents. The EEG(electroencephalography) during a monotonous task was measured, and it was investigated how these measures change under the low arousal (drowsy) state. The time series of mean power frequency of EEG was plotted on Xbar control chart. Under the low arousal state (drowsy state), the mean power frequency tended to be lower than central line (CL) and range between CL and lower control limit (LCL). Under the worst case, the mean power frequency was lower than LCL. The ratio of such intervals to total measurement period tended to increase under drowsy state. The mean power frequency was found to be effective for evaluating drowsiness of drivers.
Driver Drowsiness Immediately before Crashes–A Comparative Investigation of EEG Pattern Recognition
Periodogram and other spectral power estimation methods are established in quantitative EEG analysis. Their outcome in case of drowsy subjects fulfilling a sustained attention task is difficult to interpret. Two novel kind of EEG analysis based on pattern recognition were proposed recently, namely the microsleep (MS) and the alpha burst (AB) pattern recognition. We compare both methods by applying them to the same experimental data and relating their output variables to two independent variables of driver drowsiness. The latter was an objective lane tracking performance variable and the first was a subjective variable of self-experienced sleepiness. Results offer remarkable differences between both EEG analysis methodologies. The expected increase with time since sleep as well as with time on task, which also exhibited in both independent variables, was not identified after applying AB recognition. The EEG immediately before fatigue related crashes contained both patterns. MS patterns were remarkably more frequent before crashes; almost every crash (98.5 %) was preceded by MS patterns, whereas less than 64 % of all crashes had AB patterns within a 10 sec pre-crash interval.
An Affordable Approach for Detecting Drivers’ Drowsiness using EEG Signal Analysis
Traffic-related reports identify drowsiness as an important factor in traffic accidents. Mitigating traffic-related accidents due to drowsiness requires a driver's drowsiness monitoring system, followed by an alert mechanism. This paper proposes a new technique that instantaneously detects whether the driver is awake or in stage-one sleep condition. The proposed technique is based on a real-time system that monitors and analyzes the EEG signal of the driver using a single dry-sensor EEG headset. Using this system, we were able to detect the driver's state in real time. When the EEG waves of the driver match that of the stageone sleep, the system produces an audible alarm to alert him. The system has reached an average accuracy of 97.6% detecting sleep in a sample of 60 subjects using the statistical characteristics of the EEG waves. Compared with similar approaches, the proposed approach is more affordable, time-efficient and less complex.
Indicators of Sleepiness in an ambulatory EEG study of night driving
… in Medicine and …, 2006
Driver sleepiness due to sleep deprivation is a causative factor in 1% to 3% of all motor vehicle crashes. In recent studies, the importance of developing driver fatigue countermeasure devices has been stressed, in order to help prevent driving accidents and errors. Although numerous physiological indicators are available to describe an individual's level of alertness, the EEG signal has been shown to be one of the most predictive and reliable, since it is a direct measure of brain activity. In the present study, multichannel EEG data that were collected from 20 sleep-deprived subjects during real environmental conditions of driving are presented for the first time. EEG data's annotation made by two independent Medical Doctors revealed an increase of slowing activity and an acute increase of the alpha waves 5-10 seconds before driving events. From the EEG data that were collected, the Relative Band Ratio (RBR) of the EEG frequency bands, the Shannon Entropy, and the Kullback-Leibler (KL) Entropy were estimated for each one second segment. The mean values of these measurements were estimated for 5 minutes periods. Analysis revealed a significant increase of alpha waves relevant band ratios (RBR), a decrease of gamma waves RBR, and a significant decrease of KL entropy when the first and the last 5-min periods were compared. A rapid decrease of both Shannon and K-L entropies was observed just before the driving events. Conclusively, EEG can assess effectively the brain activity alterations that occur a few seconds before sleeping/drowsiness events in driving, and its quantitative measurements can be used as potential sleepiness Manuscript
EEG-Based Index for Timely Detecting User’s Drowsiness Occurrence in Automotive Applications
Frontiers in Human Neuroscience
Human errors are widely considered among the major causes of road accidents. Furthermore, it is estimated that more than 90% of vehicle crashes causing fatal and permanent injuries are directly related to mental tiredness, fatigue, and drowsiness of the drivers. In particular, driving drowsiness is recognized as a crucial aspect in the context of road safety, since drowsy drivers can suddenly lose control of the car. Moreover, the driving drowsiness episodes mostly appear suddenly without any prior behavioral evidence. The present study aimed at characterizing the onset of drowsiness in car drivers by means of a multimodal neurophysiological approach to develop a synthetic electroencephalographic (EEG)-based index, able to detect drowsy events. The study involved 19 participants in a simulated scenario structured in a sequence of driving tasks under different situations and traffic conditions. The experimental conditions were designed to induce prominent mental drowsiness in the fin...
2005
The management of fatigue is increasingly considered a serious public health and safety concern because impaired vigilance is believed to be a primary contributor to transportation and industrial accidents. Military operations are particularly vulnerable to the effects of fatigue due to the irregular nature of mission-related schedules and the stress of combat conditions. The ability to monitor levels of alertness in real-time, coupled with feedback to the operator or a third party, could prevent accidents and save lives. The electroencephalogram (EEG) is widely regarded as the physiological "gold standard" for the assessment of alertness. This study explored the feasibility of an integrated approach that combined real-time quantification of EEG indices and audio feedback alarms to assist fourteen healthy participants in overcoming performance deficits on neurocognitive tests and in a driving simulator task during a sleep deprivation session. As expected, sleep deprivation significantly increased drowsiness as measured by B-Alert EEG classifications and impairments in neurocognitive tests and driving simulator performance. Timely administration of feedback resulted in increased alertness as measured by changes in EEG indices and performance, particularly during driving simulator task. Most participants reported that the feedback alarms were beneficial in helping them maintain alertness. This suggests that a closed-loop EEG-based system combined with intelligent feedback can improve performance and decrease operator errors resulting from fatigue.
Clinical …, 2007
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Electroencephalogram-Based Approaches for Driver Drowsiness Detection and Management: A Review
Sensors, 2022
Drowsiness is not only a core challenge to safe driving in traditional driving conditions but also a serious obstacle for the wide acceptance of added services of self-driving cars (because drowsiness is, in fact, one of the most representative early-stage symptoms of self-driving carsickness). In view of the importance of detecting drivers’ drowsiness, this paper reviews the algorithms of electroencephalogram (EEG)-based drivers’ drowsiness detection (DDD). To facilitate the review, the EEG-based DDD approaches are organized into a tree structure taxonomy, having two main categories, namely “detection only (open-loop)” and “management (closed-loop)”, both aimed at designing better DDD systems that ensure early detection, reliability and practical utility. To achieve this goal, we addressed seven questions, the answers of which helped in developing an EEG-based DDD system that is superior to the existing ones. A basic assumption in this review article is that although driver drowsin...
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2014
Sleep deprivation and/or a high workload situation can adversely affect driving performance, decreasing a driver's capacity to respond effectively in dangerous situations. In this context, to provide useful feedback and alert signals in real time to the drivers physiological and brain activities have been increasingly investigated in literature. In this study, we analyze the increase of cerebral workload and the insurgence of drowsiness during car driving in a simulated environment by using high resolution electroencephalographic techniques (EEG) as well as neurophysiologic variables such as heart rate (HR) and eye blinks rate (EBR). The simulated drive tasks were modulated with five levels of increasing difficulty. A workload index was then generated by using the EEG signals and the related HR and EBR signals. Results suggest that the derived workload index is sensitive to the mental efforts of the driver during the different drive tasks performed. Such workload index was based...
An overview of several methods of electroencephalography (EEG) analysis in order to assess driver sleepiness is presented. All methods were applied to one single data set obtained from overnight driving simulations in our lab. 10 young adults (age 22.4 ± 4.1 years) participated and drove on rural roads; time on task was 7 x 40 min and time since sleep ranged between 16 and 22 hours. Results show large inter-individual variability of all variables and moderate correlation coefficients to one subjective and one objective independent variable of driver drowsiness. Only one method, the detection of microsleep-like EEG patterns, provides a variable with strong increases immediately before sleepiness related crashes. It is concluded that EEG analysis should attach more importance to shortterm patterns and should renounce the analysis of spectral power in four bands.