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BCIT Traffic Complexity
Overview: The Traffic Complexity study was designed to collect extended time-on-task measurements of subjects performing a driving task in a simulated environment in order to assess fatigue-based performance through novel biomarkers. Similar to the Baseline Driving study, the Speed Control study was intended to identify periods of driver fatigue via predictive algorithms formulated from the analysis of driver EEG data, in comparison to the objective performance measures, and in contrast with the (non-fatigued) Calibration driving session for the subject. Traffic Complexity extended the paradigm by modulating the visual complexity and the frequency of perturbation events vs. Baseline Driving.
Further information is available on request from cancta.net.
Subjects: Volunteers from the local community recruited through advertisements.
Apparatus: Driving simulator with steering wheel and brake / foot pedals (Real Time Technologies; Dearborn, MI); Video Refresh Rate (VRR) = 900 Hz; Vehicle data log file Sampling Rate (SR) = 100 Hz); EEG (BioSemi 64 (+8) channel systems with 4 eye and 2 mastoid channels recorded; SR=2048 Hz); Eye Tracking (Sensomotoric Instruments (SMI); REDEYE250).
Initial setup: Upon arrival to the lab, subjects were given an introduction to the primary study for which they were recruited and provided informed consent and provided demographics information. This was followed by a practice session, to acclimate the subject to the driving simulator. The driving practice task lasted 10-15 min, until asymptotic performance in steering and speed control was demonstrated and lack of motion sickness was reported. Subjects were then outfitted and prepped for eye tracking and EEG acquisition.
Task organization: Subjects would perform the Baseline Driving task and the Traffic Complexity task, with counter-balancing used across subjects as to which of them came first. The Baseline Driving run was 45 minutes of continuous driving, with subjects responsible for speed and steering control. Both driving tasks were conducted on the same simulated long, straight road. The Baseline run was done in a visually sparse environment, and the Traffic Complexity runs included pedestrians and other traffic. In each case, the subject was instructed to stay within the boundaries of the right-most lane, and to drive at the posted speed limits.
The vehicle was periodically subject to lateral perturbing forces, which could be applied to either side of the vehicle, pushing the vehicle out of the center of the lane; and the subject was instructed to execute corrective steering actions to return the vehicle to the center of the lane.
Independent variables: Visual Complexity (high vs. low), Perturbation Frequency (high vs. low).
Dependent variables: Reaction times to perturbations, continuous performance based on vehicle log (steering wheel angle, lane position, heading error, etc.), Task-Induced Fatigue Scale (TIFS), Karolinska Sleepiness Scale (KSS), Visual Analog Scale of Fatigue (VAS-F).
Note: Questionnaire data is available upon request from cancta.net.
Additional data acquired: Participant Enrollment Questionnaire, Subject Questionnaire for Current Session, Simulator Sickness Questionnaire.
Experimental Locations: Teledyne Corporation, Durham, NC.
Note 1: This dataset has a corresponding dataset in the BCIT Calibration Driving ds004118 which has the 15 minute driving task performed prior to this one.
Note 2: This dataset has a corresponding dataset in the BCIT Baseline Driving ds004120 which was a longer driving task in a sparse environment.
- Jonathan Touryan (data and curation)
- Greg Apker (data)
- Brent Lance (data)
- Scott Kerick (data)
- Anthony Ries (data)
- Justin Brooks (data)
- Kaleb McDowell (data)
- Tony Johnson (curation)
- Kay Robbins (curation)
Uploaded byKay Robbins on 2022-05-03 - 20 days ago
Last Updated2022-05-04 - 19 days ago
How to AcknowledgeGarcia, J.O., Brooks, J., Kerick, S., Johnson, T., Mullen, T.R., Vettel, J.M., 2017. Estimating direction in brain-behavior interactions: Proactive and reactive brain states in driving. NeuroImage 150, 239?249. https://doi.org/10.1016/j.neuroimage.2017.02.057.
- This research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-10-0-0002.
References and Links
- Touryan, J., Apker, G., Lance, B.J., Kerick, S.E., Ries, A.J., McDowell, K., 2014. Estimating endogenous changes in task performance from EEG. Front. Neurosci. 8. https://doi.org/10.3389/fnins.2014.00155.
- Brooks, J., Kerick, S., 2015. Event-related alpha perturbations related to the scaling of steering wheel corrections. Physiol. Behav. 149, 287?293. https://doi.org/10.1016/j.physbeh.2015.05.026.
- Brooks, J.R., Kerick, S.E., McDowell, K., 2015. Novel measure of driver and vehicle interaction demonstrates transient changes related to alerting. J. Mot. Behav. J. Mot. Behav. 47, 47, 106, 106?116. https://doi.org/10.1080/00222895.2014.959887, 10.1080/00222895.2014.959887.
- Garcia, J.O., Brooks, J., Kerick, S., Johnson, T., Mullen, T.R., Vettel, J.M., 2017. Estimating direction in brain-behavior interactions: Proactive and reactive brain states in driving. NeuroImage 150, 239?249. https://doi.org/10.1016/j.neuroimage.2017.02.057.
- Bigdely-Shamlo, N., Touryan, J., Ojeda, A., Kothe, C., Mullen, T., Robbins, K., 2019. Automated EEG mega-analysis I: Spectral and amplitude characteristics across studies, NeuroImage, p. 116361, https://doi.org/10.1016/j.neuroimage.2019.116361.
- Bigdely-Shamlo, N., Touryan, J., Ojeda, A., Kothe, C., Mullen, T., Robbins, K., 2019. Automated EEG mega-analysis II: Cognitive aspects of event related features, NeuroImage, p. 116054, https://doi.org/10.1016/j.neuroimage.2019.116054.