{"dataset":{"id":"55222","dataset_id":"on004123","name":"BCIT Traffic Complexity","description":"This dataset contains EEG, eye-tracking, and vehicle performance data collected during the BCIT Traffic Complexity study, in which subjects performed a simulated driving task under varying visual complexity and perturbation frequency conditions. The study aimed to identify biomarkers of driver fatigue by comparing EEG-based predictive algorithms to objective performance measures and subjective fatigue scales. Data collection took place at Teledyne Corporation in Durham, NC, and is related to companion datasets on Baseline Driving and Calibration Driving tasks.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on004123","concept_doi":"10.82901/nemar.on004123","latest_version_doi":"10.82901/nemar.on004123.v1.0.0","created_at":"2026-06-23 11:32:04","updated_at":"2026-08-19 16:25:34","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"BCIT Traffic Complexity\",\n  \"description\": \"This dataset contains EEG, eye-tracking, and vehicle performance data collected during the BCIT Traffic Complexity study, in which subjects performed a simulated driving task under varying visual complexity and perturbation frequency conditions. The study aimed to identify biomarkers of driver fatigue by comparing EEG-based predictive algorithms to objective performance measures and subjective fatigue scales. Data collection took place at Teledyne Corporation in Durham, NC, and is related to companion datasets on Baseline Driving and Calibration Driving tasks.\",\n  \"methods_description\": \"EEG was recorded using BioSemi 64+8 channel systems (including eye and mastoid channels) at a sampling rate of 2048 Hz. Eye tracking was performed with a Sensomotoric Instruments REDEYE250 system, and vehicle data logs were sampled at 100 Hz using a Real Time Technologies driving simulator with a video refresh rate of 900 Hz. Subjects performed Baseline Driving and Traffic Complexity tasks in counter-balanced order, with the vehicle subject to lateral perturbing forces requiring corrective steering. Fatigue was assessed using the Task-Induced Fatigue Scale, Karolinska Sleepiness Scale, and Visual Analog Scale of Fatigue.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Jonathan Touryan (data and curation)\": {},\n    \"Greg Apker (data)\": {},\n    \"Brent Lance (data)\": {},\n    \"Scott Kerick (data)\": {},\n    \"Anthony Ries (data)\": {},\n    \"Justin Brooks (data)\": {},\n    \"Kaleb McDowell (data)\": {},\n    \"Tony Johnson (curation)\": {},\n    \"Kay Robbins (curation)\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"Fatigue\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D005221\"\n    },\n    {\n      \"term\": \"Automobile Driving\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D001334\"\n    },\n    {\n      \"term\": \"Eye tracking\"\n    },\n    {\n      \"term\": \"driving simulator\"\n    },\n    {\n      \"term\": \"brain-computer interface\"\n    },\n    {\n      \"term\": \"cognitive workload\"\n    },\n    {\n      \"term\": \"fatigue biomarkers\"\n    },\n    {\n      \"term\": \"perturbation paradigm\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.3389/fnins.2014.00155.\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1016/j.physbeh.2015.05.026.\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1080/00222895.2014.959887, 10.1080/00222895.2014.959887.\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1016/j.neuroimage.2017.02.057.\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1016/j.neuroimage.2019.116361.\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1016/j.neuroimage.2019.116054.\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on004123\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1016/j.neuroimage.2017.02.057\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.3389/fnins.2014.00155\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1016/j.physbeh.2015.05.026\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1080/00222895.2014.959887\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1016/j.neuroimage.2019.116361\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1016/j.neuroimage.2019.116054\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds004123.v1.0.0\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds004118\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsPartOf\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds004120\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsPartOf\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/on004123\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"Army Research Laboratory\",\n      \"award_number\": \"W911NF-10-0-0002\"\n    }\n  ],\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"18.8 GB (50 files)\"\n  ],\n  \"formats\": [\n    \".ipynb\",\n    \".json\",\n    \".md\",\n    \".set\",\n    \".tsv\",\n    \".txt\",\n    \".xlsx\",\n    \".yml\"\n  ],\n  \"source_hash\": \"86daadcb1bee51fec045c0a046a6952ceab1c086c4ef3727a43c038465a6eb7f\"\n}","last_activity_at":"2026-06-23 11:32:04","source":"openneuro","source_id":"ds004123","subject_count":29,"modalities":"eeg","age_min":null,"age_max":null,"file_size":18814364475,"total_files":302,"tasks":"DriveWithComplexity","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":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)","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on004123-blue)](https://doi.org/10.82901/nemar.on004123)\n\n## BCIT Traffic Complexity\r\n\r\n### Introduction\r\n\r\n**Overview:** The Traffic Complexity study was designed to collect extended time-on-task measurements of\r\nsubjects performing a driving task in a simulated environment in order to assess fatigue-based performance\r\nthrough novel biomarkers. Similar to the Baseline Driving study, the Speed Control study was intended to\r\nidentify periods of driver fatigue via predictive algorithms formulated from the analysis of driver EEG data,\r\nin comparison to the objective performance measures, and in contrast with the (non-fatigued)\r\nCalibration driving session for the subject. Traffic Complexity extended the paradigm by modulating\r\nthe visual complexity and the frequency of perturbation events vs. Baseline Driving.\r\n\r\nFurther information is available on request from [cancta.net](https://cancta.net).\r\n\r\n### Methods   \r\n\r\n**Subjects:** Volunteers from the local community recruited through advertisements.  \r\n \r\n**Apparatus:**  Driving simulator with steering wheel and brake / foot pedals (Real Time Technologies; Dearborn, MI);\r\nVideo Refresh Rate (VRR) = 900 Hz; Vehicle data log file Sampling Rate (SR) = 100 Hz);\r\nEEG (BioSemi 64 (+8) channel systems with 4 eye and 2 mastoid channels recorded; SR=2048 Hz);\r\nEye Tracking (Sensomotoric Instruments (SMI); REDEYE250).\r\n\r\n**Initial setup:** Upon arrival to the lab, subjects were given an introduction to the primary study\r\nfor which they were recruited and provided informed consent and provided demographics information.\r\nThis was followed by a practice session, to acclimate the subject to the driving simulator.\r\nThe driving practice task lasted 10-15 min, until asymptotic performance in steering and speed control\r\nwas demonstrated and lack of motion sickness was reported.\r\nSubjects were then outfitted and prepped for eye tracking and EEG acquisition.\r\n\r\n**Task organization:** Subjects would perform the Baseline Driving task and the Traffic Complexity task,\r\nwith counter-balancing used across subjects as to which of them came first.\r\nThe Baseline Driving run was 45 minutes of continuous driving, with subjects responsible\r\nfor speed and steering control. Both driving tasks were conducted on the same simulated long,\r\nstraight road. The Baseline run was done in a visually sparse environment, and the Traffic Complexity\r\nruns included pedestrians and other traffic. In each case, the subject was instructed to stay\r\nwithin the boundaries of the right-most lane, and to drive at the posted speed limits.\r\n\r\nThe vehicle was periodically subject to lateral perturbing forces, which could be applied to either\r\nside of the vehicle, pushing the vehicle out of the center of the lane; and the subject was instructed\r\nto execute corrective steering actions to return the vehicle to the center of the lane.\r\n\r\n**Independent variables:** Visual Complexity (high vs. low), Perturbation Frequency (high vs. low).\r\n\r\n**Dependent variables:** Reaction times to perturbations, continuous performance based on vehicle\r\nlog (steering wheel angle, lane position, heading error, etc.), Task-Induced Fatigue Scale (TIFS),\r\nKarolinska Sleepiness Scale (KSS), Visual Analog Scale of Fatigue (VAS-F).\r\n\r\n**Note:** Questionnaire data is available upon request from [cancta.net](https://cancta.net).\r\n\r\n**Additional data acquired:** Participant Enrollment Questionnaire, Subject Questionnaire for Current Session,\r\nSimulator Sickness Questionnaire.\r\n\r\n**Experimental Locations:**  Teledyne Corporation, Durham, NC.\r\n\r\n**Note 1:** This dataset has a corresponding dataset in the BCIT Calibration Driving ds004118 which has the\r\n15 minute driving task performed prior to this one.\r\n\r\n**Note 2:** This dataset has a corresponding dataset in the BCIT Baseline Driving ds004120 which was a \r\nlonger driving task in a sparse environment.","bids_version":"1.7.0","sessions_count":1,"publish_date":null,"embedding_dirty":0,"license_tier":"public","zarr_status":"failed","zarr_converted_at":null,"zarr_store_count":null,"zarr_index_etag":null,"zarr_source_commit":null,"archive_status":"ready","archive_size":18373425023,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":null,"zarr_errors":30,"zarr_failure_count":30,"zarr_deterministic":1,"zarr_failed_at":"2026-08-05 21:33:43","num_dataset_citations":0,"num_datapaper_citations":39,"n_channels":64,"electrode_system":"10-10","has_hed":1,"hed_version":"8.0.0","is_exemplar":0,"bytes_present":18813138851,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":null,"archive_absent_files":null,"archive_declared_files":null,"zarr_pool_breaks":null,"total_recording_duration":null,"recording_duration_min":null,"recording_duration_max":null,"recording_count":null,"recordings_unavailable":null,"recordings_measured":null,"channel_count_min":null,"channel_count_max":null,"sampling_frequency":1024,"power_line_frequency":60,"eeg_reference":"CMS","placement_scheme":"Custom","sweep_stamps":"{\"enrichment_updated_at\":\"2026-08-19 16:25:19\",\"metadata_updated_at\":\"2026-08-19 16:25:33\",\"archive_checked_at\":\"2026-06-23 11:52:27\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-06-23 11:43:59\",\"citations_updated_at\":\"2026-09-09 03:00:07\",\"channel_montage_checked_at\":\"2026-06-28 23:20:41\",\"hed_checked_at\":\"2026-06-30 04:53:26\",\"data_checked_at\":\"2026-08-06 03:01:03\",\"availability_report_at\":\"2026-07-23 01:17:09\",\"recording_stats_at\":null,\"signal_defaults_at\":\"2026-09-02 12:08:18\"}","participants":29,"num_citations":39,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"nemarAdmin","owner_github":"nemarAdmin","file_size_formatted":"17.52 GB","zarr_data_failures":{"count":30,"detail_ref":"zarr/index.json","compacted_by":"migration_0074"},"zarr_index_url":null,"attestation_deposit_type":null,"attestation_key_status":null,"attestation_deidentified":null,"attestation_no_duplicate":null,"attestation_upstream_source":null,"attestation_accepted_at":null}}