{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a331f","dataset_id":"ds004123","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.ds004123.v1.0.0","datatypes":["eeg"],"demographics":{"subjects_count":29,"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/ds004123","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":"b509f1a014490d0b8727fb13c27c123f33ac65b0e97ba557e40a41854796f336","license":"CC0","n_contributing_labs":null,"name":"BCIT Traffic Complexity","readme":"## BCIT Traffic Complexity\n### Introduction\n**Overview:** The Traffic Complexity 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. Similar to the Baseline Driving study, the Speed Control study was intended to\nidentify periods of driver fatigue via predictive algorithms formulated from the analysis of driver EEG data,\nin comparison to the objective performance measures, and in contrast with the (non-fatigued)\nCalibration driving session for the subject. Traffic Complexity extended the paradigm by modulating\nthe visual complexity and the frequency 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).\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 control\nwas demonstrated and lack of motion sickness was reported.\nSubjects were then outfitted and prepped for eye tracking and EEG acquisition.\n**Task organization:** Subjects would perform the Baseline Driving task and the Traffic Complexity task,\nwith counter-balancing used across subjects as to which of them came first.\nThe Baseline Driving run was 45 minutes of continuous driving, with subjects responsible\nfor speed and steering control. Both driving tasks were conducted on the same simulated long,\nstraight road. The Baseline run was done in a visually sparse environment, and the Traffic Complexity\nruns included pedestrians and other traffic. In each case, the subject was instructed to stay\nwithin the boundaries of the right-most lane, and to drive at the posted speed limits.\nThe vehicle was periodically subject to lateral perturbing forces, which could be applied to either\nside of the vehicle, pushing the vehicle out of the center of the lane; and the subject was instructed\nto execute corrective steering actions to return the vehicle to the center of the lane.\n**Independent variables:** Visual Complexity (high vs. low), Perturbation Frequency (high vs. low).\n**Dependent variables:** Reaction times to perturbations, continuous performance based on vehicle\nlog (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 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:** This dataset has a corresponding dataset in the BCIT Baseline Driving ds004120 which was a\nlonger driving task in a sparse environment.","recording_modality":["eeg"],"senior_author":"Kay Robbins (curation)","sessions":["01"],"size_bytes":18814102906,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["DriveWithComplexity"],"timestamps":{"digested_at":"2026-04-22T12:26:09.699660+00:00","dataset_created_at":"2022-05-03T14:10:02.137Z","dataset_modified_at":"2022-05-04T15:34:31.000Z"},"total_files":30,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004123","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv","task-DriveWithComplexity_eeg.json","task-DriveWithComplexity_events.json"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"dfa23b305e34f32c","model":"openai/gpt-5.2","tagged_at":"2026-01-20T10:31:32.179850+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Attention"],"confidence":{"pathology":0.8,"modality":0.85,"type":0.75},"reasoning":{"few_shot_analysis":"Most similar labeling convention is the few-shot example \"EEG: DPX Cog Ctl Task in Acute Mild TBI\" (labeled Type=Attention, Modality=Visual), where participants respond to task demands over time and the construct is cognitive control/attention rather than pure sensory perception. The current dataset likewise emphasizes sustained task performance/fatigue monitoring during an ongoing visually guided task.\n\nA contrasting convention is the few-shot \"EEG Motor Movement/Imagery Dataset\" (Type=Motor), where the explicit research focus is motor execution/imagery itself. In the present dataset, motor actions (steering/braking) are required, but the stated aim is fatigue-based performance biomarkers during time-on-task driving rather than motor physiology per se, which pushes Type toward Attention rather than Motor.","metadata_analysis":"Key population/task/stimulus facts from the README:\n- Population: \"Subjects: Volunteers from the local community recruited through advertisements.\"\n- Task context: \"subjects performing a driving task in a simulated environment in order to assess fatigue-based performance through novel biomarkers\"\n- Stimulus/input emphasis: \"Traffic Complexity extended the paradigm by modulating the visual complexity\" and \"Independent variables: Visual Complexity (high vs. low)\"\n- Task demand/construct: \"collect extended time-on-task measurements\" and \"identify periods of driver fatigue via predictive algorithms formulated from the analysis of driver EEG data\"","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n1) Metadata says: participants are \"Volunteers from the local community\" with no diagnosis mentioned.\n2) Few-shot pattern suggests: non-clinical volunteer samples map to Healthy (e.g., multiple few-shots explicitly describe healthy controls/participants).\n3) ALIGN.\n\nModality:\n1) Metadata says: the paradigm manipulates \"visual complexity\" and is a \"driving task in a simulated environment\" with traffic/pedestrians.\n2) Few-shot pattern suggests: tasks dominated by visual stimuli/environment map to Visual (e.g., DPX task labeled Visual; gambling/learning tasks labeled Visual).\n3) ALIGN.\n\nType:\n1) Metadata says: aim is \"assess fatigue-based performance\" and \"collect extended time-on-task measurements\" to \"identify periods of driver fatigue\".\n2) Few-shot pattern suggests: sustained performance/cognitive control tasks are labeled Attention (DPX example), whereas explicit movement/imagery focus is labeled Motor (EEG motor movement/imagery example).\n3) Mostly ALIGN with Attention; minor ambiguity due to continuous steering/braking (motor component), but the stated research purpose centers on vigilance/fatigue monitoring rather than motor execution.","decision_summary":"Top-2 candidates per category with head-to-head comparison:\n\nPathology:\n- Healthy (winner): Supported by \"Volunteers from the local community recruited through advertisements.\" No clinical recruitment criteria stated.\n- Unknown (runner-up): would apply if population health status were unspecified and no inference allowed, but metadata strongly implies a normative volunteer cohort.\nAlignment: few-shot conventions and metadata align.\n\nModality:\n- Visual (winner): Supported by \"driving task in a simulated environment\" and explicit manipulation \"modulating the visual complexity\" / \"Independent variables: Visual Complexity\".\n- Motor (runner-up): driving involves steering/braking, but modality is defined by stimulus/input channel; the dominant manipulated input is visual scene complexity.\nAlignment: aligns with few-shot convention mapping visually driven tasks to Visual.\n\nType:\n- Attention (winner): Supported by \"extended time-on-task measurements\" and goal to \"assess fatigue-based performance\" / \"identify periods of driver fatigue\"—consistent with vigilance/sustained attention constructs.\n- Motor (runner-up): continuous steering/braking could motivate Motor, but motor behavior is mainly the means to assess fatigue-related performance rather than the research focus.\nAlignment: aligns with few-shot convention where cognitive control/vigilance tasks are Attention unless movement physiology/imagery is primary.\n\nConfidence justification (quotes/features):\n- Pathology: based on explicit volunteer recruitment quote and lack of diagnosis mention.\n- Modality: multiple explicit mentions of visual complexity manipulation and simulated driving visual environment.\n- Type: explicit fatigue/time-on-task purpose statements; remaining ambiguity with motor demands lowers confidence slightly."}},"nemar_citation_count":0,"computed_title":"BCIT Traffic Complexity","nchans_counts":[{"val":74,"count":30}],"sfreq_counts":[{"val":1024.0,"count":30}],"stats_computed_at":"2026-04-22T23:16:00.307100+00:00","total_duration_s":null,"canonical_name":null,"name_confidence":0.42,"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_Traffic"}}