{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a331e","dataset_id":"ds004122","associated_paper_doi":null,"authors":["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)"],"bids_version":"1.7.0","contact_info":["Kay Robbins","Jonathan Touryan"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds004122.v1.0.0","datatypes":["eeg"],"demographics":{"subjects_count":32,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds004122","osf_url":null,"github_url":null,"paper_url":null},"funding":["This research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-10-0-0002."],"ingestion_fingerprint":"03d29679e4d5ab2b6a7d3e8acd51b9fb9739e842634bd1ec07eb2f8213b5e631","license":"CC0","n_contributing_labs":null,"name":"BCIT Speed Control","readme":"## BCIT Speed Control\n### Introduction\n**Overview:** The Speed Control study was designed to collect extended time-on-task measurements of subjects\nperforming a driving task in a simulated environment in order to assess fatigue-based performance\nthrough novel biomarkers. Similar to the Baseline Driving study, the Speed Control study was intended\nto identify periods of driver fatigue via predictive algorithms formulated from the analysis of driver\nEEG data, in comparison to the objective performance measures, and in contrast with the (non-fatigued)\nCalibration driving session for the subject. Speed Control extended the paradigm by modulating driver\ncontrol of the throttle and increasing the frequency and magnitude of perturbation events vs. Baseline Driving.\nFurther information is available on request from [cancta.net](https://cancta.net).\n### Methods\n**Subjects:** Volunteers from the local community recruited through advertisements.\n**Apparatus:**  Driving simulator with steering wheel and brake / foot pedals (Real Time Technologies; Dearborn, MI);\nVideo Refresh Rate (VRR) = 900 Hz; Vehicle data log file Sampling Rate (SR) = 100 Hz);\nEEG (BioSemi 64 (+8) channel systems with 4 eye and 2 mastoid channels recorded; SR=2048 Hz);\nEye Tracking (Sensomotoric Instruments (SMI); REDEYE250). Eye tracking data is not included with this dataset.\n**Initial setup:** Upon arrival to the lab, subjects were given an introduction to the primary study\nfor which they were recruited and provided informed consent and provided demographics information.\nThis was followed by a practice session, to acclimate the subject to the driving simulator.\nThe driving practice task lasted 10-15 min, until asymptotic performance in steering and speed\ncontrol was demonstrated and lack of motion sickness was reported. Subjects were then outfitted\nand prepped for eye tracking and EEG acquisition.\n**Task organization:** Subjects performed the Speed Control condition A and Speed Control condition B,\nwith counter-balancing used across subjects as to which of them came first.\nSpeed Control A was 45 minutes of continuous driving, with subjects responsible for steering control,\nwith the simulator maintaining a constant speed automatically.\nSpeed Control B was similar, but the subject was responsible for both steering and maintaining speed.\nBoth driving tasks were conducted on the same simulated long, straight road.\nIn each case, the subject was instructed to stay within the boundaries of the right-most lane,\nand to drive at the posted speed limits. The vehicle was periodically subject to lateral perturbing forces,\nwhich could be applied to either side of the vehicle, pushing the vehicle out of the center of the lane;\nand the subject was instructed to execute corrective steering actions to return the vehicle to the center of the lane.\n**Independent variables:** Speed Control (cruise vs. manual).\n**Dependent variables:** Reaction times to perturbations, continuous performance based on vehicle log\n(steering wheel angle, lane position, heading error, etc.), Task-Induced Fatigue Scale (TIFS),\nKarolinska Sleepiness Scale (KSS), Visual Analog Scale of Fatigue (VAS-F).\n**Note:** questionnaire data is available upon request from [cancta.net](https://cancta.net).\n**Additional data acquired:** Participant Enrollment Questionnaire, Subject Questionnaire for Current Session,\nSimulator Sickness Questionnaire.\n**Experimental Locations:** Teledyne Corporation, Durham, NC.\n**Note:** This dataset has a corresponding dataset in the BCIT Calibration Driving ds004118 which has the\n15 minute driving task performed prior to this one.","recording_modality":["eeg"],"senior_author":"Kay Robbins (curation)","sessions":["01"],"size_bytes":38826707124,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["Drive"],"timestamps":{"digested_at":"2026-04-22T12:26:09.297312+00:00","dataset_created_at":"2022-05-03T13:04:29.013Z","dataset_modified_at":"2022-05-04T22:05:50.000Z"},"total_files":63,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004122","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv","task-Drive_eeg.json","task-Drive_events.json"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"e477192150199a30","model":"openai/gpt-5.2","tagged_at":"2026-01-20T10:30:58.322702+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Attention"],"confidence":{"pathology":0.8,"modality":0.7,"type":0.75},"reasoning":{"few_shot_analysis":"Most similar few-shot convention is the DPX cognitive control dataset (labeled Type=Attention) where sustained cognitive control/monitoring is the primary construct; here the driving task is explicitly used to assess fatigue-based performance, which maps best to Attention rather than Motor (even though steering responses occur). For Pathology, multiple few-shots label community/volunteer cohorts without diagnoses as Healthy (e.g., 'EEG: Three armed bandit gambling task', 'EEG Motor Movement/Imagery Dataset'). For Modality, the few-shots show that modality is based on stimulus/input channel (e.g., visual discrimination -> Visual), not response mechanics; the driving simulator implies predominantly visual scene input even with motor output.","metadata_analysis":"Key quoted facts:\n1) Population: \"Subjects: Volunteers from the local community recruited through advertisements.\" (no diagnosis or patient group mentioned)\n2) Task/construct: \"performing a driving task in a simulated environment in order to assess fatigue-based performance\" and \"identify periods of driver fatigue via predictive algorithms formulated from the analysis of driver EEG data\"\n3) Stimulus/task environment: \"Driving simulator with steering wheel and brake / foot pedals\" and \"Both driving tasks were conducted on the same simulated long, straight road\" and \"periodically subject to lateral perturbing forces\".\nThese indicate a healthy volunteer driving-simulation fatigue/vigilance paradigm with continuous visual road/lane stimuli and perturbation events requiring corrective steering.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: \"Volunteers from the local community\" (no clinical recruitment criteria stated).\n- Few-shot pattern suggests: non-clinical volunteer cohorts -> Healthy.\n- ALIGN.\n\nModality:\n- Metadata says: \"driving task in a simulated environment\" on a \"simulated long, straight road\" (implying visually guided driving), plus \"lateral perturbing forces\".\n- Few-shot pattern suggests: modality follows stimulus/input channel; visually presented tasks -> Visual.\n- ALIGN (with minor ambiguity because perturbations add proprioceptive/tactile components).\n\nType:\n- Metadata says: \"assess fatigue-based performance\" and \"identify periods of driver fatigue\" using performance measures and sleepiness/fatigue scales (KSS, VAS-F).\n- Few-shot pattern suggests: fatigue/vigilance and continuous performance monitoring map best to Attention rather than Motor.\n- ALIGN.","decision_summary":"Top-2 candidates and selection:\n\nPathology:\n1) Healthy (selected) — Evidence: \"Volunteers from the local community recruited through advertisements.\" No patient/diagnosis terms anywhere.\n2) Unknown — would apply if population health status were not implied; however community volunteer recruitment strongly supports Healthy.\nConfidence rationale: one strong explicit recruitment description and no competing clinical indicators.\n\nModality:\n1) Visual (selected) — Evidence: \"driving task in a simulated environment\"; \"simulated long, straight road\"; lane keeping and speed-limit following are visually guided.\n2) Multisensory — Evidence: \"lateral perturbing forces\" and physical control interfaces (steering wheel/pedals) add non-visual inputs.\nConfidence rationale: multiple cues indicate a visually dominant simulator task, but explicit sensory stimulus descriptions are limited.\n\nType:\n1) Attention (selected) — Evidence: \"assess fatigue-based performance\"; \"identify periods of driver fatigue\"; long continuous (45 min) driving with reaction times and sleepiness/fatigue scales (KSS, VAS-F) targets vigilance/sustained attention.\n2) Motor — Evidence: continuous steering/throttle control and corrective steering actions.\nConfidence rationale: fatigue/vigilance assessment is the stated scientific aim, making Attention the better fit than Motor."}},"nemar_citation_count":0,"computed_title":"BCIT Speed Control","nchans_counts":[{"val":74,"count":63}],"sfreq_counts":[{"val":1024.0,"count":63}],"stats_computed_at":"2026-04-22T23:16:00.307087+00:00","total_duration_s":null,"author_year":"Touryan2022_BCIT_Speed","canonical_name":null}}