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BCIT Mind Wandering
Overview: Subjects in the Mind Wandering study performed a long-duration simulated driving task with perturbations and audio stimuli in a visually complex environment. The purpose of this effort was to supplement and extend the related driving research to collect prolonged 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 Mind Wandring 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.
Mind Wandering extended the paradigm by adding different types of background audio (task relevant, non-task relevant, internal focus) and a vigilance task (identify police vehicles), in addition to increasing perturbation magnitude and frequency 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 within the study: Subjects always began recording sessions by performing a Calibration Driving task, which was a 15-minute drive where the subject controlled only the steering (and speed was controlled by the simulator).
Mind wandering task details: Subjects would perform Mind Wandering conditions A, B, and C, with counter-balancing used across subjects as to which of them came first.
Mind Wandering A was 30 minutes of continuous driving, with subjects responsible for steering and maintaining speed, while task relevant audio (traffic safety) played in the background. Subjects were instructed to look for police vehicles and respond by pressing a button on the steering wheel.
Mind Wandering B and C were similar, with non-task relevant audio (e.g. sports broadcast) in B and internal focus audio (mindfulness breathing exercise) in C. Both driving tasks were conducted on the same simulated long, straight road, that contained a mix of regular traffic and police vehicles.
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: Background Audio (task relevant vs. non-task relevant vs. internal focus).
Dependent variables: Reaction times to perturbations, continuous performance based on vehicle log (steering wheel angle, lane position, heading error, etc.), reaction times to target vehicles (police), 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 Location: Teledyne Corporation, Durham, NC.
Note: This dataset has a corresponding dataset in the BCIT Calibration Driving ds004118 which has the 15 minute driving task prior to this one.
- 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 Updated2022-05-03 - 20 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