{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a331c","dataset_id":"ds004120","associated_paper_doi":null,"authors":["Jonathan Touryan (data and curation)","Greg Apker (data)","Brent Lance (data)","Scott Kerick (data)","Anthony Ries (data)","Kaleb McDowell (data)","Tony Johnson (curation)","Kay Robbins (curation)"],"bids_version":"1.7.0","contact_info":["Jonathan Touryan","Kay Robbins"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds004120.v1.0.0","datatypes":["eeg"],"demographics":{"subjects_count":109,"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/ds004120","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":"a7fa2420fd1066adee0e953100871d2c60dcf8def1962a6c91e89dfd68f73986","license":"CC0","n_contributing_labs":null,"name":"BCIT Baseline Driving","readme":"## BCIT Baseline Driving\n### Introduction\n**Overview:** The Baseline Driving study was designed to collect extended time-on-task measurements of\nsubjects performing a driving task in a simulated environment in order to assess fatigue-based performance\nthrough novel biomarkers. The Baseline Driving study was intended to identify periods of driver fatigue\nvia predictive algorithms formulated from the analysis of driver EEG data, in comparison to the objective\nperformance measures, and in contrast with the (non-fatigued) Calibration driving session for the subject.\nBaseline driving data sets were designed to be the second component of every recording session within the\nBCIT program, which featured multiple studies investigating fatigue.\nCollectively, the Baseline Driving recordings comprise a virtual study, in which long time-on-task\ndriving performance can be analyzed for fatigue-related EEG biomarkers based on measured driving\nperformance degradation. Further information is available on request from [cancta.net](https://cancta.net).\nThe task was performed using identical systems at three different sites:\n- Army Research Laboratory, Aberdeen MD (T1)\n- Teledyne Corporation, Durham, NC (T2)\n- Science Applications International Corporation (SAIC), Louisville, CO (T3)\nAll sites used identical driving simulator setups.\nThe data collected at site T1 used a 64-channel Biosemi EEG headset as did the data collected at site T2,\nwhile site T3 used a 256-channel Biosemi EEG headset.\nData from site T1 has legacy subject IDs in the range 1000 to 1999.\nData from site T2 has legacy subject IDs in the range 2000 to 2999.\nData from site T3 has legacy subject IDs in the range 3000 to 3999.\nLegacy subject IDs are unique across the entire BCIT program.\n### Methods\n**Subjects:** Subjects at Aberdeen Proving Grounds were recruited, on a voluntary basis from among\nthe scientists and engineers working at APG.\nSubjects recruited by Teledyne and SAIC were found via advertising and community outreach efforts,\nand primarily consisted of local college students.\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 256 (+8) channel systems with 4 eye and 2 mastoid channels recorded; SR=1024 Hz);\nEye Tracking (Sensomotoric Instruments (SMI); REDEYE250). Eye tracking data is not included in this dataset.\n**Initial setup:** Upon arrival to the lab, subjects were given an introduction to the primary\nstudy for 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 always began recording sessions by performing a Calibration Driving task,\nwhich was a 15-minute drive where the subject controlled only the steering (and speed was controlled by the simulator).\nFollowing this, subjects would perform the Baseline Driving task and the Guard Duty task,\nwith counter-balancing used across subjects as to which of them came first.\nThe Baseline Driving run was 60 minutes of driving, performed in 6 blocks of 10 minutes each,\nwith subjects responsible for speed and steering control.\nThe 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:** For T1 (ARL) and T3 (SAIC) there were no independent variables.\nFor T2 data sets (Teledyne), independent variables were Visual Complexity (high vs. low),\nPerturbation Frequency (high vs. low).\n**Dependent variables:** Reaction times to perturbations, continuous performance based\non vehicle log (steering wheel angle, lane position, heading error, etc.),\nTask-Induced Fatigue Scale (TIFS), Karolinska Sleepiness Scale (KSS), Visual Analog Scale of Fatigue (VAS-F).\nNote: 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:**  Army Research Laboratory, Aberdeen MD (site T1);\nTeledyne Corporation, Durham, NC (site T2);\nScience Applications International Corporation (SAIC), Louisville, CO (site T3).\n**Note 1:** This dataset has a corresponding dataset in the BCIT Calibration Driving ds004118 which has the\n15 minute driving task performed prior to this one.\n**Note 2:** Some of the subjects in this dataset performed either the BCIT Basic Guard Duty Task (ds004118) or\nthe BCIT Advanced Guard Duty Task (ds004106) counterbalanced during the same session.","recording_modality":["eeg"],"senior_author":"Kay Robbins (curation)","sessions":["01","02","03"],"size_bytes":324791400217,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["DriveWithSpeedChange"],"timestamps":{"digested_at":"2026-04-22T12:26:08.732528+00:00","dataset_created_at":"2022-05-03T00:06:41.150Z","dataset_modified_at":"2022-05-04T22:29:32.000Z"},"total_files":131,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004120","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv","task-DriveWithSpeedChange_events.json"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"99d61f74d07a97b1","model":"openai/gpt-5.2","tagged_at":"2026-01-20T10:30:00.660655+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Attention"],"confidence":{"pathology":0.7,"modality":0.8,"type":0.8},"reasoning":{"few_shot_analysis":"Most similar convention in the few-shot set is the DPX cognitive control dataset in acute mild TBI, labeled Type=Attention (sustained cognitive control/monitoring demands during a task). The present dataset similarly focuses on sustained task performance degradation and vigilance over long time-on-task (driving for 60 minutes) to detect fatigue, which is typically mapped to Attention rather than Motor or Perception in these conventions. For Modality, the motor imagery example shows that response/movement does not define Modality; instead, the dominant stimulus channel does—here the simulated driving scene and manipulated 'Visual Complexity' suggest Visual.","metadata_analysis":"Key task/purpose statements indicate a fatigue/vigilance driving paradigm: (1) \"collect extended time-on-task measurements of subjects performing a driving task in a simulated environment in order to assess fatigue-based performance\" and (2) \"intended to identify periods of driver fatigue via predictive algorithms formulated from the analysis of driver EEG data\". Stimulus/apparatus context indicates primarily a visually presented simulation: (1) \"Driving simulator with steering wheel and brake / foot pedals\" and (2) \"independent variables were Visual Complexity (high vs. low)\". Population recruitment is described without any diagnosis-based recruitment: (1) \"Subjects ... recruited ... from among the scientists and engineers\" and (2) \"primarily consisted of local college students.\"","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata SAYS participants were recruited from workplace/community/college students (\"scientists and engineers\"; \"local college students\") with no disorder mentioned. Few-shot pattern SUGGESTS that when no clinical cohort is specified, label Healthy. ALIGN.\nModality: Metadata SAYS the task is a simulated driving task with manipulated \"Visual Complexity\" and a driving simulator setup. Few-shot pattern SUGGESTS choosing stimulus modality (e.g., Visual for screen-based tasks) rather than the motor response. ALIGN.\nType: Metadata SAYS the goal is fatigue detection and performance degradation over long time-on-task (\"assess fatigue-based performance\"; \"identify periods of driver fatigue\"). Few-shot pattern SUGGESTS sustained performance/vigilance constructs map best to Attention (as in cognitive control/vigilance tasks) rather than Motor. ALIGN.","decision_summary":"Pathology top-2: (A) Healthy—supported by recruitment described with no diagnosis focus (\"scientists and engineers\"; \"local college students\") and the general few-shot convention that non-clinical cohorts default to Healthy. (B) Unknown—possible if population health status is not explicitly stated. Winner: Healthy (no evidence of clinical recruitment).\nModality top-2: (A) Visual—supported by \"driving task in a simulated environment\" and explicit manipulation of \"Visual Complexity\" plus simulator context. (B) Multisensory—driving involves motor actions and possible proprioceptive/force cues, but these are not the dominant described stimulus manipulations. Winner: Visual.\nType top-2: (A) Attention—supported by the time-on-task fatigue/vigilance framing (\"extended time-on-task\"; \"identify periods of driver fatigue\"; performance degradation metrics). (B) Other—could be broadly 'fatigue biomarker discovery' rather than a canonical cognitive construct. Winner: Attention, following few-shot convention for sustained performance/fatigue paradigms."}},"nemar_citation_count":0,"computed_title":"BCIT Baseline Driving","nchans_counts":[{"val":266,"count":81},{"val":74,"count":50}],"sfreq_counts":[{"val":1024.0,"count":109},{"val":2048.0,"count":22}],"stats_computed_at":"2026-04-22T23:16:00.307060+00:00","total_duration_s":null,"canonical_name":null,"name_confidence":0.32,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"canonical","author_year":"Touryan2022_BCIT_Baseline"}}