Event Abstract Back to Event Predicting response latency using EEG alpha-band power and low-cost wearable physiological sensors. Dean Cisler1*, Pamela M. Greenwood1, Ryan McKendrick2 and Carryl L. Baldwin1 1 George Mason University, United States 2 Northrop Grumman (United States), United States According to the National Highway Traffic Safety Administration (NHTSA), over 37,000 people died in traffic crashes in 2016 (NHTSA, 2017). Human error plays a role in a substantial portion of these fatalities with some estimates as high as 94% (Singh, 2015). Advanced driver assistance systems (ADASs, e.g., adaptive cruise control and automatic emergency braking) have the potential to improve driver performance and occupant safety. Automatic emergency braking has been found to reduce rear-end crashes by about 40% (Cicchino, 2017). Although ADASs do reduce driver workload, drivers may consequently become complacent and inattentive. Automated systems are not infallible and over-trust in those systems could lead the driver to fail to monitor or detect critical signals related to system reliability (Parasuraman & Manzey, 2010). Inattention in a partially autonomous vehicle can be hazardous, as current ADASs are not designed to brake effectively during “cut-in,” “cut-out,” and crossing-path scenarios. Now that most new vehicles are equipped with some ADASs, it is important to understand how drivers respond to signals of automation unreliability. Inattentive drivers may require more urgent warnings – warnings that could annoy or startle the attentive driver. One approach to monitoring driver attentional state is to use non-invasive physiological measures. Past research used EEG, eye-tracking, and heart rate variability (HRV) to classify driver states (Hogervorst, Brouwer & van Erp, 2014). Physiological measures can determine whether a driver is on-task or mind wandering (Baldwin, et al., 2017) and can successfully adapt automation to improve performance in an unmanned aerial vehicle task (Wilson & Russell, 2003a, b). EEG alpha-band power can predict driver errors (O’Connell et al., 2009) and predict mind wandering (Baldwin, et al., 2017). However, EEG recording is impractical for real-world driving. Driver attentional state could be monitored with low-cost physiological metrics of gaze dispersion and heart rate variability, previously found to predict speed of “take-over” from automation (Dehais, Causse, & Tremblay, 2011). The current study tested the hypothesis that HRV and gaze dispersion measured with wearable technology (ZephyrTM BioPatch HP Monitoring Device and Pupil Headset by Pupil Labs, respectively) would be as effective as EEG alpha-band power in predicting performance during a simulated fully autonomous lane-change task. We used backward stepwise multiple regression to test the hypothesis that the wearable technology metrics (eye gaze, HRV) would predict the performance data as well as the EEG measure of alpha-band power. Task. The participants operated a driving simulator for five autonomous drives (roughly 11 minutes each). During the drives, an automation interface was presented in the lower right of the windshield composed of right or left facing arrows (170 ms) with varying amounts of red and green. The automation “reliable” arrow tip was both red and green while the automation “unreliable” arrow tip was all red. The participant’s task during the drives was to identify cues indicating potential automation failures (i.e., the unreliable arrows) by pressing a button on the steering wheel when automation unreliable arrows were detected. Reliable automation arrows were presented on 90% of the trials while unreliable arrows were presented on the remaining 10% of trials. Following unreliable arrows, 60% of the time the vehicle did not change lanes, 20% of the time the vehicle made an incorrect lane change, 20% of the time the vehicle made a correct lane change. The participant was asked to make serial button presses to indicate (1) automation unreliability and (2) the error the vehicle made. Before the experiment, participants were equipped with an eye-tracker, heart-rate monitor, and connected to a 40-channel NuAmps EEG system (Fz, Cz, Pz, Oz, P3, P4, F3, F4, R and L mastoids). In addition to responding to the interface arrows, participants were required to (a) keep a running count of billboards and (b) answer situation awareness questions presented on the screen. Response time (RT) to the unreliable arrows, alpha-band at Pz, vertical and horizontal gaze dispersion HRV, and billboard and situation awareness responses were assessed. HRV was calculated as the root mean square of successive differences 10s prior to each unreliable stimulus. Horizontal and vertical gaze dispersion were calculated by taking the log transform of the standard deviation of eye movements 10s before the stimuli (lnX and lnY). Results. Data were analyzed using SPSS. Insofar as performance accuracy was at ceiling for each participant, RT was the behavioral measure of interest. To compare the physiological metrics, we used a backwards regression predicting RT from Cz Alpha and Pz Alpha, HRV, lnY and lnX. This analysis showed that HRV explained 11.3% of the variance (R2 = .113, F(1, 25) = 4.321, p < 0.05) and was the only factor to significantly predict RT (β = 0.384, p < 0.05). Discussion. These results indicate wearable, low fidelity technology show promise for predicting the speed with which a driver in an autonomous vehicle will respond to signals of automation failure. Future applications may be able to use low cost wearables capable of calculating HRV in real time to signal periods of driver inattention. Further work is needed to determine the most efficient method of reorienting drivers’ attention during automation failures. Keywords: alpha-band, Wearable Technology, Autonomous Driving, response latency, Predicting Behavior Conference: 2nd International Neuroergonomics Conference, Philadelphia, PA, United States, 27 Jun - 29 Jun, 2018. Presentation Type: Poster Presentation Topic: Neuroergonomics Citation: Cisler D, Greenwood PM, McKendrick R and Baldwin CL (2019). Predicting response latency using EEG alpha-band power and low-cost wearable physiological sensors.. Conference Abstract: 2nd International Neuroergonomics Conference. doi: 10.3389/conf.fnhum.2018.227.00056 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 11 Apr 2018; Published Online: 27 Sep 2019. * Correspondence: Mr. Dean Cisler, George Mason University, Fairfax, United States, dcisler@masonlive.gmu.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Dean Cisler Pamela M Greenwood Ryan McKendrick Carryl L Baldwin Google Dean Cisler Pamela M Greenwood Ryan McKendrick Carryl L Baldwin Google Scholar Dean Cisler Pamela M Greenwood Ryan McKendrick Carryl L Baldwin PubMed Dean Cisler Pamela M Greenwood Ryan McKendrick Carryl L Baldwin Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.