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Annals of Advances in Automotive Medicine
Identifying Periods of Drowsy Driving Using EEG Timothy Brown, Ph.D.1, Robin Johnson, Ph. D.2, Gary Milavetz, Pharm.D.3 1
National Advanced Driving Simulator, Center for Computer Aided Design, The University of Iowa, Iowa City, Iowa 2
Advanced Brain Monitoring, Carlsbad, California
3
College of Pharmacy, The University of Iowa, Iowa City, Iowa
__________________________________ 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. __________________________________
INTRODUCTION A survey conducted in 2003 found that 37% of drivers self-report having fallen asleep for at least a moment (nodded off) while driving at least once in their driving career, while 8% of them had done it in the last six months. According to the National Sleep Foundation’s 2009 annual Sleep in America survey, 28 percent of drivers had driven while drowsy at least once per month in the past year. Of those that drove while drowsy, 28% self-report having fallen asleep (National Sleep Foundation, 2009). More recent data, from the AAA Foundation, indicates this trend continues with 32% of drivers reporting driving while drowsy during the preceding month, and that 10% of drivers admitted having fallen asleep in the past year and 41% at some point in their lifetime (Arnold & Tefft, 2012). Drowsy driving is not only common in the United States, it was found that one in five (20%) Canadian drivers have admitted to nodding off or falling asleep
at least once while driving (Beirness, Simpson, & Desmond, 2005) and that driver fatigue contributes to at least 9% to 10% of crashes in the UK (Maycock, 1997). These international data point to a traffic safety problem that leads to a significant number of injuries and fatalities every year on our nation’s and the world’s highways. Crashes associated with drowsy driving were thought to account for 2-3% of total accidents, however recent work at the Virginia Tech Transportation Institute indicate that this may be closer to 20%(Dingus et al., 2006). Others have found that those with chronic drowsiness (as measured with an objective laboratory test, the MSLT) had significantly greater risk of being in an accident (12-15x that found in non-drowsy individuals) (Drake et al., 2010). Given that there are 83,000 (NHTSA, 2011) to 270,000 (Royal, 2003) crashes annually in the US, that result in an estimated886 drowsiness related fatalities annually (NHTSA, 2011), addressing drowsy driving has great
57th AAAM Annual Conference Annals of Advances in Automotive Medicine September 22-25, 2013
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Vol 57 • September 2013 public health and safety implications. Clearly, there is cause for concern about the rate of drowsy driving and the resultant crashes, injuries and fatalities. This concern creates a need for research to facilitate the development of technological approaches that will reduce the number of lives lost due to drowsy driving. However, one of the challenges in addressing this problem is accurately determining when drivers are drowsy and when they are alert so that the effectiveness of the technological interventions can be assessed. Ground truth, an unambiguous indicator of the driver state, is difficult to achieve for level of drowsiness. Self assessment of drowsiness is generally flawed, with most drivers underreporting their drowsiness level (Moller, Kayumov, Bulmash, Nhan, & Shapiro, 2006; Sharwood et al., 2012). Use of eye tracking of eyelid measures provides an unobtrusive measurement of current driver state, but represents a physical manifestation of driver state rather than the actual driver state. These systems also suffer from failure due to face orientation, illumination inconsistencies in the environment, the distance of the subject from the camera, and a lack of dismounted/head mounted system (Liu, Xu, & Fujimura, 2002). Video coding of the driver state using an agreed upon metric provides another approach that could be used, but similar to automatic eye tracking, it is an outward sign of an inward state. The use of EEG provides the nearest measurement of internal driver state (Berka et al., 2008; Johnson et al., 2011). Although there can be challenges associated with study design, as EEG has underlying effects for gender (Brenner, Ulrich, & Reynolds, 1995; Volf & Razumnikova, 1999), age (Polich, 1997), and diurnal variations (Cacot, Tesolin, & Sebban, 1995), it provides the most accurate assessment of the driver state with low risk of failures associated with eye tracking approaches. EEG has been applied in highly structured laboratory experiences of drowsiness and driving for several decades, and in on-the-road applications for over a decade. However, the early on-the-road systems were bulky and distracting to the driver. Current technology has allowed . the B-Alert and other systems to overcome these issue and provide high quality data in a variety of settings (Davis, Popovic, Johnson, Berka, & Mitrovic, 2009; Levendowski, Berka, & Konstantinovic, 2002; Stevens, Galloway, & Berka, 2007; Stevens et al., 2007) with a light weight, invisible to the user system.
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The overall purpose of this line of research was to investigate ways to identify degraded driving performance associated with drowsy driving within an overall program to identify driver inattention and impairment. This paper will focus on the ability of EEG systems designed for research applications to identify periods of drowsy driving through algorithmic analysis of the raw EEG data.
METHOD Participants Data were collected from 72 volunteer drivers from three age groups (21-34, 38-51, and 55-68 years of age) driving through representative situations on three types of roadways (urban, freeway, and rural). These age groups were chosen for consistency with a prior study using a similar protocol that examined alcohol impairment (Lee et al., 2010) to facilitate comparisons between the data. In this analysis actual age was used rather than the age groups. Participants drove at three times of day (9 am-1 pm, 10 pm-2 am, and 2 am – 6 am) to induce different levels of drowsiness. These time periods were chosen to provide a daytime drive prior to the afternoon circadian dip associated with increased drowsiness, and to provide two nighttime drives with one being during the period when participants would typically be going to sleep, and the later during what would typically be REM sleep. Participants were assigned an order during the window based upon the time they typically awoke in an attempt to balance continuous time awake across participants. To be eligible, participants were required to: possess a valid US driver’s license; have been a licensed driver for two or more years; drive at least 10,000 miles per year; have no restrictions on driver’s license except for vision; be in good general health; have a normal nighttime sleep pattern; and not require the use of any special equipment to drive. These criteria were chosen for consistency with prior driving impairment research being conducted in this area (Brown, Lee, Schwarz, Fiorentino, & McDonald, Under Review) to facilitate comparisons between data sets.
Procedure An initial telephone interview was conducted to determine eligibility for the study. Applicants were screened in terms of chronic illnesses that could confound interpretation of the data or put the participant at risk, current health status, medication
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and drug usage, morning/evening chronotypes (Adan & Almirall, 1991), and sleep apnea history (Brown, Dow, Trask, Dyken, & Salisbury, 2009). Pregnancy, disease, or evidence of sleep apnea or nocturnal chronotype were excluded from the study as were those taking prescription medications that cause drowsiness. Each participant had three visits (screening, daytime drive, overnight drives). The two overnight drives occurred on a single night. The daytime and nighttime data collection visits were separated by three days and the order of these visits and scenario event sequence were counterbalanced. On study Visit 1 (screening), obtained consent was obtained from each participant. They then provided a urine sample for the drug screen and, for females, the pregnancy screen. During a five-minute period following these activities, the participant sat alone in the room where subsequent measurements of blood pressure, heart rate, height, and weight were made. If participants met study criteria, they completed demographic surveys, and were trained concerning the drive and the in-vehicle tasks prior to completing the practice drive prior to completing surveys about how they felt and about the realism of the simulator. If the participant remained eligible, baseline EEG measurements were recorded. Benchmark EEG assessment included simultaneous EEG and two passive vigilance tasks (pace keeping to an auditory or visual signal stimuli) and one active vigilance task (three choice vigilance task requiring that the participant decide if the stimuli is a target, non-target or interference stimuli), that require approximately 5 min each to complete (Johnson et al., 2011). The EEG data from these tasks is then used to calibrate the B-Alert drowsiness algorithm to the individual (Johnson et al., 2011). The algorithm then provides an epoch by epoch probability of sleep onset, distraction, drowsiness, and engagement. Prior to their study visits, participants were provided with activity monitors and activity logs to verify sleep preceding the visits. During the daytime visit, participants were asked to not ingest any caffeine or other stimulant drugs. Logs were reviewed to verify a normal night’s sleep (at least six hours) the preceding night. Their BAC was checked to ensure that they were not under the influence of alcohol. Participants were then fitted with the wireless B-Alert X-10 EEG system (Advanced Brain Monitoring, 2011; Johnson et al., 2011) to record their EEG and heart rate. The participants then entered the simulator and eye
Annals of Advances in Automotive Medicine
tracking calibrations were completed. Prior to beginning the drive, the participants also completed a questionnaire about their current sleepiness level, the Stanford Sleepiness Scale (SSS) (Hoddes, Zarcone, Smythe, Phillips, & Dement, 1973) to document subject sleepiness on a scale of one to seven, and a version of the Psychomotor Vigilance Task or PVT (Cognitive Media, Iowa City, IA) based on the Psychomotor Vigilance Task (Loh, Lamond, Dorrian, Roach, & Dawson, 2004; Wilkinson & Houghton, 1982) to provide an objective assessment of performance associated known to vary with drowsiness by measuring the speed with which participants respond to a visual stimuli . The participants then completed their daytime drive in the simulator. Following the drive, participants were administered the SSS, the wellness survey, PVT, a Retrospective Sleepiness Scale (RSS), and a simulator realism survey. The RSS is an estimate from a continuous time measurement of drowsiness over the course of the drive and required subjective judgments of drowsiness using the same scale as the SSS at specified scenario locations and then connecting those points by assessing their drowsiness over the course of the drive. The B-Alert X-10 system was then removed. During the nighttime-drowsy visit, participants were instructed to restrict beverage consumption to water after 12:00 pm on the day of their overnight visit, to minimize caffeine intake. Participants were picked up at their homes after having eaten dinner, and transported to the simulation facility to arrive around 7pm. Logs were reviewed to verify a normal night’s sleep (at least six hours) the preceding night and that they did not take any naps during the day. Caffeine intake was reviewed and if caffeine was consumed after noon on the day of the overnight drive, the participant was either rescheduled or dropped from the study. Participants were then fitted with the BAlert device-10 system. A variety of activities were provided to keep participants awake including activities on an iPad, reading, playing computer games, etc. They were monitored to ensure they did not fall asleep or converse with other participants. If participants began to fall asleep, they were engaged by a researcher to keep them awake. The participants completed the SSS every 30 minutes until they drove. One hour prior to their drive, they were taken to a private room to wait. They completed a PVT at this time, and also at 30 minutes prior to the drive.
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Vol 57 • September 2013 Participants were escorted to the simulator between 22:00 and 01:00 for their first drives. Once in the simulator, eye tracking calibration procedures were performed, and the EEG sensor impedance and artifact signals were reviewed to ensure good data signal. Immediately before the drive, participants completed a PVT and SSS. After the drive, participants completed the SSS, a Wellness Survey, a PVT, and a Retrospective Sleepiness Scale.
the signals are accomplished with miniaturized electronics in a portable unit worn on the head (see Figure 3). The combination of amplification and digitization of the EEG close to the sensors and secure wireless transmission of the data facilitates the acquisition of high quality signals even in high electromagnetic interference environments (Advanced Brain Monitoring, 2011).
Simulator Scenario Participants were then escorted back to a separate waiting area where TV, movies, reading, computer games, etc. were available. A SSS was administered every 30 minutes until their next drive. One hour prior to their second drive times, participants were again taken to a private room to wait. They completed a PVT one hour prior to the drive and also at 30 minutes prior to the drive. Participants were escorted to the simulator between 02:00 and 05:00 for their second drives. Once in the simulator, eye tracking calibration procedures were performed, and the EEG signal quality was again reviewed. Before starting the drive, the participants completed a PVT and SSS. After the drive, participants completed SSS, a Wellness Survey, a PVT, a retrospective sleepiness scale, and a realism survey. The B-Alert X-10 system was then removed and they were transported home.
Apparati
Each drive was composed of three nighttime driving segments lasting a total of approximately 45 minutes. The drives started with an urban segment composed of a two-lane roadway through a city with posted speed limits of 25 to 45 with signal-controlled and uncontrolled intersections. An interstate segment followed that consisted of a four-lane divided expressway with a posted speed limit of 70 mph. Following a period in which drivers followed the vehicle ahead, they encountered infrequent lane changes associated with the need to pass several slower-moving trucks. The drives concluded with a rural segment that was composed of a two-lane undivided road with curves; followed by a gravel road segment; and then a 10-minute section of straight rural driving. Three equivalent versions of the scenario were created to minimize learning effects.
The National Advanced Driving Simulator (NADS), shown in Figure 1, made it possible to collect representative driving behavior data from drowsy drivers in a safe and controlled manner. This is the highest fidelity simulator in the United States and allowed for precise characterization of driver response. Drivers’ control inputs, vehicle state, driving context, and driver state were captured in representative driving situations (see Figure 2). It consist of an 11 degree of freedom motion base including high frequency actuators for road feel, a full size vehicle cab, 360 degree visuals, and a 3d audio system. The B-Alert X-10 EEG System was used to collect EEG data. The system is designed with fixed sensor locations for three head sizes (small, medium and large) with placement determined according to the International 10 – 20 system coordinates. Sensor site locations, based on the standard 10-20 electrode placement, on the B-Alert X10 system included: Fz, F3, F4, Cz, C3, C4, P3, P4, POz, as well as ECG. Acquisition from these sites is monopolar (referenced to linked mastoids), with bi-polar channels available through offline processing. Amplification, digitization and radio frequency (RF) transmission of
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Figure 1. simulator.
The NADS-1 high-fidelity driving
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Figure 2. An urban driving scene from the NADS-1 simulator.
Figure 3. B-Alert X-10 EEG Headset
Data Reduction and Statistics The paper surveys that were used to collect the RSS scales were scanned into JPG files. These files were then processed using a Matlab® (The Mathworks, Inc., Natick, MA) script to automatically quantify the RSS data into discrete points across the drive resulting in approximately one point every 30 seconds across the drive. Interpolation between these points was used to provide RSS data corresponding to each data point collected in the simulator. This data was further reduced by taking the average across each event to provide an estimate of level of drowsiness for each participant for each event for each drive. Ninety-five percent confidence intervals were calculated for each event for each drive. EEG metrics were extracted from the raw signal at two levels. First, a Fast Fourier transformation was performed to extract power spectral densities, and the densities from 8-12 Hz were averaged to acquire Alpha activity on an epoch by epoch level. The epoch by epoch EEG probabilities of sleep onset, distraction, and drowsiness were extracted by entering the power spectral densities from Fz, Cz,
Annals of Advances in Automotive Medicine
and POz for FzPOz and CzPOz into the proprietary algorithm described in detail in Johnson, et al., 2011. The workload metric is extracted in a similar manner, but requires channels Fz, Cz, POz, F3, C3, and C4, to calculate differentials FzPOz, CzPOz, CzF3, FzC4, and C3C4 to calculate the algorithm (Berka et al., 2004, 2007) The epoch level data was then averaged for either the duration of each subtask while driving (i.e. while on gravel, etc), or across the entire driving session. Additionally, as alpha and theta are the power spectral ranges most typically associated with fatigue or drowsiness (Craig, Tran, Wijesuriya, & Nguyen, 2012), Theta activity was also collected and analyzed. The study design introduced a great deal of variability in the EEG signal due to diurnal variation, even within the driving session periods, as each subjects started their 45 min drive at varying times within the 2-4 hr window, and they did not complete the successive drives in the same order. In addition, the age range of participants was broad, and this is also known to alter EEG in a systematic manner. As a result, we used an ANCOVA for all EEG analysis, with age and minutes from start of drive session (i.e. 9a,, 10 pm, or 2 am) as covariates. A one-way ANCOVA was then used to compare day to early night to late night, or across terrain type. In addition a two-way ANCOVA (time of day X task) was used to identify tasks most at risk for drowsiness.
RESULTS Drowsiness Across the Drive The responses from the subjects concerning their level of sleepiness across the drive were assessed for the three drives. Figure 4 shows the ratings of sleepiness drivers made after they completed each drive using the retrospective sleepiness scale (RSS). Each line represents the ratings of a single driver. Over the course of each drive, the level of drowsiness tends to increase. As can be seen in the figure, there is considerable variability across events and across subjects. In general the RSS indicate that subjective drowsiness is greater for the night conditions compared to the daytime drive, but that the biggest change comes with the transition to late night. The reported sleepiness varies considerably with some drivers in the late night condition reporting lower levels of sleepiness compared to those in the daytime condition. Some drivers in the late night condition are quite alert and some in the daytime condition are quite drowsy.
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Figure 4. Retrospective sleepiness ratings across the drive. Note that the final two points are a division of the prior dark rural segment. Each line represents a single driver and each point represents the mean with a 95% confidence interval. (Figure from Brown, Lee, Schwarz, Fiorentino, & McDonald (Under Review))
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Annals of Advances in Automotive Medicine
EEG Measurement of Drowsiness EEG metrics were evaluated to determine their utility in future predictive studies. Time of day had significant main effect on the B-Alert sleep onset metric, F(2,70) = 8.1, p < .001 with the greatest probability occurring during early evening drives (see ).
Figure 5. Main effect of EEG based sleep onset algorithm by time of day. In analyzing alpha and theta activity, the central/lateral sites (C3, C4) and parietal area (POz, P3, P4) were examined to better understand how the time of day of driving effects the power spectral ranges most associated with fatigue or drowsiness. As is illustrated in Figure 6, power spectral ranges were greater in the early and late night in lateral central sites (C4, C3) for both alpha and theta wave forms, Fs(2,70) ≥ 11.66, p < .001 and parietal area for alpha; F(2,70) = 6.31, p < .001. When looking at effects across the driving events, significant difference were observed for probability of sleep onset, F(137,4304) = 1.24, p
Annals of Advances in Automotive Medicine
Identifying Periods of Drowsy Driving Using EEG Timothy Brown, Ph.D.1, Robin Johnson, Ph. D.2, Gary Milavetz, Pharm.D.3 1
National Advanced Driving Simulator, Center for Computer Aided Design, The University of Iowa, Iowa City, Iowa 2
Advanced Brain Monitoring, Carlsbad, California
3
College of Pharmacy, The University of Iowa, Iowa City, Iowa
__________________________________ 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. __________________________________
INTRODUCTION A survey conducted in 2003 found that 37% of drivers self-report having fallen asleep for at least a moment (nodded off) while driving at least once in their driving career, while 8% of them had done it in the last six months. According to the National Sleep Foundation’s 2009 annual Sleep in America survey, 28 percent of drivers had driven while drowsy at least once per month in the past year. Of those that drove while drowsy, 28% self-report having fallen asleep (National Sleep Foundation, 2009). More recent data, from the AAA Foundation, indicates this trend continues with 32% of drivers reporting driving while drowsy during the preceding month, and that 10% of drivers admitted having fallen asleep in the past year and 41% at some point in their lifetime (Arnold & Tefft, 2012). Drowsy driving is not only common in the United States, it was found that one in five (20%) Canadian drivers have admitted to nodding off or falling asleep
at least once while driving (Beirness, Simpson, & Desmond, 2005) and that driver fatigue contributes to at least 9% to 10% of crashes in the UK (Maycock, 1997). These international data point to a traffic safety problem that leads to a significant number of injuries and fatalities every year on our nation’s and the world’s highways. Crashes associated with drowsy driving were thought to account for 2-3% of total accidents, however recent work at the Virginia Tech Transportation Institute indicate that this may be closer to 20%(Dingus et al., 2006). Others have found that those with chronic drowsiness (as measured with an objective laboratory test, the MSLT) had significantly greater risk of being in an accident (12-15x that found in non-drowsy individuals) (Drake et al., 2010). Given that there are 83,000 (NHTSA, 2011) to 270,000 (Royal, 2003) crashes annually in the US, that result in an estimated886 drowsiness related fatalities annually (NHTSA, 2011), addressing drowsy driving has great
57th AAAM Annual Conference Annals of Advances in Automotive Medicine September 22-25, 2013
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Vol 57 • September 2013 public health and safety implications. Clearly, there is cause for concern about the rate of drowsy driving and the resultant crashes, injuries and fatalities. This concern creates a need for research to facilitate the development of technological approaches that will reduce the number of lives lost due to drowsy driving. However, one of the challenges in addressing this problem is accurately determining when drivers are drowsy and when they are alert so that the effectiveness of the technological interventions can be assessed. Ground truth, an unambiguous indicator of the driver state, is difficult to achieve for level of drowsiness. Self assessment of drowsiness is generally flawed, with most drivers underreporting their drowsiness level (Moller, Kayumov, Bulmash, Nhan, & Shapiro, 2006; Sharwood et al., 2012). Use of eye tracking of eyelid measures provides an unobtrusive measurement of current driver state, but represents a physical manifestation of driver state rather than the actual driver state. These systems also suffer from failure due to face orientation, illumination inconsistencies in the environment, the distance of the subject from the camera, and a lack of dismounted/head mounted system (Liu, Xu, & Fujimura, 2002). Video coding of the driver state using an agreed upon metric provides another approach that could be used, but similar to automatic eye tracking, it is an outward sign of an inward state. The use of EEG provides the nearest measurement of internal driver state (Berka et al., 2008; Johnson et al., 2011). Although there can be challenges associated with study design, as EEG has underlying effects for gender (Brenner, Ulrich, & Reynolds, 1995; Volf & Razumnikova, 1999), age (Polich, 1997), and diurnal variations (Cacot, Tesolin, & Sebban, 1995), it provides the most accurate assessment of the driver state with low risk of failures associated with eye tracking approaches. EEG has been applied in highly structured laboratory experiences of drowsiness and driving for several decades, and in on-the-road applications for over a decade. However, the early on-the-road systems were bulky and distracting to the driver. Current technology has allowed . the B-Alert and other systems to overcome these issue and provide high quality data in a variety of settings (Davis, Popovic, Johnson, Berka, & Mitrovic, 2009; Levendowski, Berka, & Konstantinovic, 2002; Stevens, Galloway, & Berka, 2007; Stevens et al., 2007) with a light weight, invisible to the user system.
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The overall purpose of this line of research was to investigate ways to identify degraded driving performance associated with drowsy driving within an overall program to identify driver inattention and impairment. This paper will focus on the ability of EEG systems designed for research applications to identify periods of drowsy driving through algorithmic analysis of the raw EEG data.
METHOD Participants Data were collected from 72 volunteer drivers from three age groups (21-34, 38-51, and 55-68 years of age) driving through representative situations on three types of roadways (urban, freeway, and rural). These age groups were chosen for consistency with a prior study using a similar protocol that examined alcohol impairment (Lee et al., 2010) to facilitate comparisons between the data. In this analysis actual age was used rather than the age groups. Participants drove at three times of day (9 am-1 pm, 10 pm-2 am, and 2 am – 6 am) to induce different levels of drowsiness. These time periods were chosen to provide a daytime drive prior to the afternoon circadian dip associated with increased drowsiness, and to provide two nighttime drives with one being during the period when participants would typically be going to sleep, and the later during what would typically be REM sleep. Participants were assigned an order during the window based upon the time they typically awoke in an attempt to balance continuous time awake across participants. To be eligible, participants were required to: possess a valid US driver’s license; have been a licensed driver for two or more years; drive at least 10,000 miles per year; have no restrictions on driver’s license except for vision; be in good general health; have a normal nighttime sleep pattern; and not require the use of any special equipment to drive. These criteria were chosen for consistency with prior driving impairment research being conducted in this area (Brown, Lee, Schwarz, Fiorentino, & McDonald, Under Review) to facilitate comparisons between data sets.
Procedure An initial telephone interview was conducted to determine eligibility for the study. Applicants were screened in terms of chronic illnesses that could confound interpretation of the data or put the participant at risk, current health status, medication
©
and drug usage, morning/evening chronotypes (Adan & Almirall, 1991), and sleep apnea history (Brown, Dow, Trask, Dyken, & Salisbury, 2009). Pregnancy, disease, or evidence of sleep apnea or nocturnal chronotype were excluded from the study as were those taking prescription medications that cause drowsiness. Each participant had three visits (screening, daytime drive, overnight drives). The two overnight drives occurred on a single night. The daytime and nighttime data collection visits were separated by three days and the order of these visits and scenario event sequence were counterbalanced. On study Visit 1 (screening), obtained consent was obtained from each participant. They then provided a urine sample for the drug screen and, for females, the pregnancy screen. During a five-minute period following these activities, the participant sat alone in the room where subsequent measurements of blood pressure, heart rate, height, and weight were made. If participants met study criteria, they completed demographic surveys, and were trained concerning the drive and the in-vehicle tasks prior to completing the practice drive prior to completing surveys about how they felt and about the realism of the simulator. If the participant remained eligible, baseline EEG measurements were recorded. Benchmark EEG assessment included simultaneous EEG and two passive vigilance tasks (pace keeping to an auditory or visual signal stimuli) and one active vigilance task (three choice vigilance task requiring that the participant decide if the stimuli is a target, non-target or interference stimuli), that require approximately 5 min each to complete (Johnson et al., 2011). The EEG data from these tasks is then used to calibrate the B-Alert drowsiness algorithm to the individual (Johnson et al., 2011). The algorithm then provides an epoch by epoch probability of sleep onset, distraction, drowsiness, and engagement. Prior to their study visits, participants were provided with activity monitors and activity logs to verify sleep preceding the visits. During the daytime visit, participants were asked to not ingest any caffeine or other stimulant drugs. Logs were reviewed to verify a normal night’s sleep (at least six hours) the preceding night. Their BAC was checked to ensure that they were not under the influence of alcohol. Participants were then fitted with the wireless B-Alert X-10 EEG system (Advanced Brain Monitoring, 2011; Johnson et al., 2011) to record their EEG and heart rate. The participants then entered the simulator and eye
Annals of Advances in Automotive Medicine
tracking calibrations were completed. Prior to beginning the drive, the participants also completed a questionnaire about their current sleepiness level, the Stanford Sleepiness Scale (SSS) (Hoddes, Zarcone, Smythe, Phillips, & Dement, 1973) to document subject sleepiness on a scale of one to seven, and a version of the Psychomotor Vigilance Task or PVT (Cognitive Media, Iowa City, IA) based on the Psychomotor Vigilance Task (Loh, Lamond, Dorrian, Roach, & Dawson, 2004; Wilkinson & Houghton, 1982) to provide an objective assessment of performance associated known to vary with drowsiness by measuring the speed with which participants respond to a visual stimuli . The participants then completed their daytime drive in the simulator. Following the drive, participants were administered the SSS, the wellness survey, PVT, a Retrospective Sleepiness Scale (RSS), and a simulator realism survey. The RSS is an estimate from a continuous time measurement of drowsiness over the course of the drive and required subjective judgments of drowsiness using the same scale as the SSS at specified scenario locations and then connecting those points by assessing their drowsiness over the course of the drive. The B-Alert X-10 system was then removed. During the nighttime-drowsy visit, participants were instructed to restrict beverage consumption to water after 12:00 pm on the day of their overnight visit, to minimize caffeine intake. Participants were picked up at their homes after having eaten dinner, and transported to the simulation facility to arrive around 7pm. Logs were reviewed to verify a normal night’s sleep (at least six hours) the preceding night and that they did not take any naps during the day. Caffeine intake was reviewed and if caffeine was consumed after noon on the day of the overnight drive, the participant was either rescheduled or dropped from the study. Participants were then fitted with the BAlert device-10 system. A variety of activities were provided to keep participants awake including activities on an iPad, reading, playing computer games, etc. They were monitored to ensure they did not fall asleep or converse with other participants. If participants began to fall asleep, they were engaged by a researcher to keep them awake. The participants completed the SSS every 30 minutes until they drove. One hour prior to their drive, they were taken to a private room to wait. They completed a PVT at this time, and also at 30 minutes prior to the drive.
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Vol 57 • September 2013 Participants were escorted to the simulator between 22:00 and 01:00 for their first drives. Once in the simulator, eye tracking calibration procedures were performed, and the EEG sensor impedance and artifact signals were reviewed to ensure good data signal. Immediately before the drive, participants completed a PVT and SSS. After the drive, participants completed the SSS, a Wellness Survey, a PVT, and a Retrospective Sleepiness Scale.
the signals are accomplished with miniaturized electronics in a portable unit worn on the head (see Figure 3). The combination of amplification and digitization of the EEG close to the sensors and secure wireless transmission of the data facilitates the acquisition of high quality signals even in high electromagnetic interference environments (Advanced Brain Monitoring, 2011).
Simulator Scenario Participants were then escorted back to a separate waiting area where TV, movies, reading, computer games, etc. were available. A SSS was administered every 30 minutes until their next drive. One hour prior to their second drive times, participants were again taken to a private room to wait. They completed a PVT one hour prior to the drive and also at 30 minutes prior to the drive. Participants were escorted to the simulator between 02:00 and 05:00 for their second drives. Once in the simulator, eye tracking calibration procedures were performed, and the EEG signal quality was again reviewed. Before starting the drive, the participants completed a PVT and SSS. After the drive, participants completed SSS, a Wellness Survey, a PVT, a retrospective sleepiness scale, and a realism survey. The B-Alert X-10 system was then removed and they were transported home.
Apparati
Each drive was composed of three nighttime driving segments lasting a total of approximately 45 minutes. The drives started with an urban segment composed of a two-lane roadway through a city with posted speed limits of 25 to 45 with signal-controlled and uncontrolled intersections. An interstate segment followed that consisted of a four-lane divided expressway with a posted speed limit of 70 mph. Following a period in which drivers followed the vehicle ahead, they encountered infrequent lane changes associated with the need to pass several slower-moving trucks. The drives concluded with a rural segment that was composed of a two-lane undivided road with curves; followed by a gravel road segment; and then a 10-minute section of straight rural driving. Three equivalent versions of the scenario were created to minimize learning effects.
The National Advanced Driving Simulator (NADS), shown in Figure 1, made it possible to collect representative driving behavior data from drowsy drivers in a safe and controlled manner. This is the highest fidelity simulator in the United States and allowed for precise characterization of driver response. Drivers’ control inputs, vehicle state, driving context, and driver state were captured in representative driving situations (see Figure 2). It consist of an 11 degree of freedom motion base including high frequency actuators for road feel, a full size vehicle cab, 360 degree visuals, and a 3d audio system. The B-Alert X-10 EEG System was used to collect EEG data. The system is designed with fixed sensor locations for three head sizes (small, medium and large) with placement determined according to the International 10 – 20 system coordinates. Sensor site locations, based on the standard 10-20 electrode placement, on the B-Alert X10 system included: Fz, F3, F4, Cz, C3, C4, P3, P4, POz, as well as ECG. Acquisition from these sites is monopolar (referenced to linked mastoids), with bi-polar channels available through offline processing. Amplification, digitization and radio frequency (RF) transmission of
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Figure 1. simulator.
The NADS-1 high-fidelity driving
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Figure 2. An urban driving scene from the NADS-1 simulator.
Figure 3. B-Alert X-10 EEG Headset
Data Reduction and Statistics The paper surveys that were used to collect the RSS scales were scanned into JPG files. These files were then processed using a Matlab® (The Mathworks, Inc., Natick, MA) script to automatically quantify the RSS data into discrete points across the drive resulting in approximately one point every 30 seconds across the drive. Interpolation between these points was used to provide RSS data corresponding to each data point collected in the simulator. This data was further reduced by taking the average across each event to provide an estimate of level of drowsiness for each participant for each event for each drive. Ninety-five percent confidence intervals were calculated for each event for each drive. EEG metrics were extracted from the raw signal at two levels. First, a Fast Fourier transformation was performed to extract power spectral densities, and the densities from 8-12 Hz were averaged to acquire Alpha activity on an epoch by epoch level. The epoch by epoch EEG probabilities of sleep onset, distraction, and drowsiness were extracted by entering the power spectral densities from Fz, Cz,
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and POz for FzPOz and CzPOz into the proprietary algorithm described in detail in Johnson, et al., 2011. The workload metric is extracted in a similar manner, but requires channels Fz, Cz, POz, F3, C3, and C4, to calculate differentials FzPOz, CzPOz, CzF3, FzC4, and C3C4 to calculate the algorithm (Berka et al., 2004, 2007) The epoch level data was then averaged for either the duration of each subtask while driving (i.e. while on gravel, etc), or across the entire driving session. Additionally, as alpha and theta are the power spectral ranges most typically associated with fatigue or drowsiness (Craig, Tran, Wijesuriya, & Nguyen, 2012), Theta activity was also collected and analyzed. The study design introduced a great deal of variability in the EEG signal due to diurnal variation, even within the driving session periods, as each subjects started their 45 min drive at varying times within the 2-4 hr window, and they did not complete the successive drives in the same order. In addition, the age range of participants was broad, and this is also known to alter EEG in a systematic manner. As a result, we used an ANCOVA for all EEG analysis, with age and minutes from start of drive session (i.e. 9a,, 10 pm, or 2 am) as covariates. A one-way ANCOVA was then used to compare day to early night to late night, or across terrain type. In addition a two-way ANCOVA (time of day X task) was used to identify tasks most at risk for drowsiness.
RESULTS Drowsiness Across the Drive The responses from the subjects concerning their level of sleepiness across the drive were assessed for the three drives. Figure 4 shows the ratings of sleepiness drivers made after they completed each drive using the retrospective sleepiness scale (RSS). Each line represents the ratings of a single driver. Over the course of each drive, the level of drowsiness tends to increase. As can be seen in the figure, there is considerable variability across events and across subjects. In general the RSS indicate that subjective drowsiness is greater for the night conditions compared to the daytime drive, but that the biggest change comes with the transition to late night. The reported sleepiness varies considerably with some drivers in the late night condition reporting lower levels of sleepiness compared to those in the daytime condition. Some drivers in the late night condition are quite alert and some in the daytime condition are quite drowsy.
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Figure 4. Retrospective sleepiness ratings across the drive. Note that the final two points are a division of the prior dark rural segment. Each line represents a single driver and each point represents the mean with a 95% confidence interval. (Figure from Brown, Lee, Schwarz, Fiorentino, & McDonald (Under Review))
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EEG Measurement of Drowsiness EEG metrics were evaluated to determine their utility in future predictive studies. Time of day had significant main effect on the B-Alert sleep onset metric, F(2,70) = 8.1, p < .001 with the greatest probability occurring during early evening drives (see ).
Figure 5. Main effect of EEG based sleep onset algorithm by time of day. In analyzing alpha and theta activity, the central/lateral sites (C3, C4) and parietal area (POz, P3, P4) were examined to better understand how the time of day of driving effects the power spectral ranges most associated with fatigue or drowsiness. As is illustrated in Figure 6, power spectral ranges were greater in the early and late night in lateral central sites (C4, C3) for both alpha and theta wave forms, Fs(2,70) ≥ 11.66, p < .001 and parietal area for alpha; F(2,70) = 6.31, p < .001. When looking at effects across the driving events, significant difference were observed for probability of sleep onset, F(137,4304) = 1.24, p
