{"dataset":{"id":"55220","dataset_id":"on004121","name":"BCIT Mind Wandering","description":"This dataset contains EEG, eye-tracking, and simulated driving performance data collected during the BCIT Mind Wandering study, in which subjects performed a prolonged simulated driving task under varying background audio conditions (task-relevant, non-task-relevant, and internal focus) while responding to periodic lateral perturbations and a vigilance task requiring detection of police vehicles. The study aimed to identify EEG-based biomarkers of driver fatigue and mind wandering by comparing extended time-on-task driving performance to a non-fatigued calibration session. Data were collected at Teledyne Laboratories under a US Army Research Laboratory contract as part of a broader effort to develop predictive fatigue algorithms from neural and behavioral signals.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on004121","concept_doi":"10.82901/nemar.on004121","latest_version_doi":"10.82901/nemar.on004121.v1.0.0","created_at":"2026-06-23 10:31:46","updated_at":"2026-08-19 16:27:48","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 Mind Wandering\",\n  \"description\": \"This dataset contains EEG, eye-tracking, and simulated driving performance data collected during the BCIT Mind Wandering study, in which subjects performed a prolonged simulated driving task under varying background audio conditions (task-relevant, non-task-relevant, and internal focus) while responding to periodic lateral perturbations and a vigilance task requiring detection of police vehicles. The study aimed to identify EEG-based biomarkers of driver fatigue and mind wandering by comparing extended time-on-task driving performance to a non-fatigued calibration session. Data were collected at Teledyne Laboratories under a US Army Research Laboratory contract as part of a broader effort to develop predictive fatigue algorithms from neural and behavioral signals.\",\n  \"methods_description\": \"Subjects performed a 15-minute Calibration Driving task followed by three 30-minute Mind Wandering conditions (A, B, C) with counterbalanced order, each featuring different background audio (task-relevant, non-task-relevant, or internal focus) during simulated driving with periodic lateral perturbations. EEG was recorded using a BioSemi 64+8 channel system (plus 4 eye and 2 mastoid channels) at 2048 Hz sampling rate, eye tracking via SMI REDEYE250, and vehicle/driving data logged at 100 Hz using a Real Time Technologies driving simulator with a 900 Hz video refresh rate. Additional questionnaire data (fatigue and sleepiness scales) were collected but are available only upon request.\",\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\": \"driving simulation\"\n    },\n    {\n      \"term\": \"mind wandering\"\n    },\n    {\n      \"term\": \"eye tracking\"\n    },\n    {\n      \"term\": \"vigilance\"\n    },\n    {\n      \"term\": \"brain-computer interface\"\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/on004121\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.3389/fnins.2014.00155\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsReferencedBy\"\n    },\n    {\n      \"identifier\": \"10.1016/j.physbeh.2015.05.026\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsReferencedBy\"\n    },\n    {\n      \"identifier\": \"10.1080/00222895.2014.959887\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsReferencedBy\"\n    },\n    {\n      \"identifier\": \"10.1016/j.neuroimage.2017.02.057\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsReferencedBy\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds004121\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsVersionOf\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds004121.v1.0.0\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/on004121\",\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    \"25.7 GB (80 files)\"\n  ],\n  \"formats\": [\n    \".ipynb\",\n    \".json\",\n    \".md\",\n    \".set\",\n    \".tsv\",\n    \".txt\",\n    \".xlsx\",\n    \".yml\"\n  ],\n  \"source_hash\": \"2cf6f359e75e215bcffe09fb3c70209a8dc5a9297f42cf70fc41b8ff22ce7310\"\n}","last_activity_at":"2026-06-23 10:31:46","source":"openneuro","source_id":"ds004121","subject_count":21,"modalities":"eeg","age_min":null,"age_max":null,"file_size":25671803658,"total_files":420,"tasks":"DriveWithTaskAudio","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.on004121-blue)](https://doi.org/10.82901/nemar.on004121)\n\n## BCIT Mind Wandering\r\n\r\n### Introduction\r\n\r\n**Overview:** Subjects in the Mind Wandering study performed a long-duration simulated driving task\r\nwith perturbations and audio stimuli in a visually complex environment.\r\nThe purpose of this effort was to supplement and extend the related driving research to collect\r\nprolonged time-on-task measurements of subjects performing a driving task in a simulated environment\r\nin order to assess fatigue-based performance through novel biomarkers. Similar to the Baseline Driving study,\r\nthe Mind Wandring study was intended to identify periods of driver fatigue via predictive algorithms formulated\r\nfrom the analysis of driver EEG data, in comparison to the objective performance measures,\r\nand in contrast with the (non-fatigued) Calibration driving session for the subject.\r\n\r\nMind Wandering extended the paradigm by adding different types of background audio\r\n(task relevant, non-task relevant, internal focus) and a vigilance task (identify police vehicles),\r\nin addition to increasing perturbation magnitude and frequency vs. baseline driving.\r\n\r\nFurther information is available on request from [cancta.net](https://cancta.net).\r\n\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\r\nstudy for 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\r\nspeed control was demonstrated and lack of motion sickness was reported.\r\n\r\nSubjects were then outfitted and prepped for eye tracking and EEG acquisition.\r\n\r\n**Task organization within the study:** Subjects always began recording sessions by performing a\r\nCalibration Driving task, which was a 15-minute drive where the subject controlled only\r\nthe steering (and speed was controlled by the simulator).\r\n\r\n**Mind wandering task details:** Subjects would perform Mind Wandering conditions\r\nA, B, and C, with counter-balancing used across subjects as to which of them came first.\r\n\r\nMind Wandering A was 30 minutes of continuous driving, with subjects responsible for\r\nsteering and maintaining speed, while task relevant audio (traffic safety) played in the background.\r\nSubjects were instructed to look for police vehicles and respond by pressing a button on the steering wheel.\r\n\r\nMind Wandering B and C were similar, with non-task relevant audio (e.g. sports broadcast) in B\r\nand internal focus audio (mindfulness breathing exercise) in C.\r\nBoth driving tasks were conducted on the same simulated long, straight road,\r\nthat contained a mix of regular traffic and police vehicles.\r\n\r\nIn each case, the subject was instructed to stay within the boundaries of the right-most lane,\r\nand to drive at the posted speed limits.\r\n\r\nThe vehicle was periodically subject to lateral perturbing forces,\r\nwhich could be applied to either side of the vehicle, pushing the vehicle out of the center\r\nof the lane; and the subject was instructed to execute corrective steering actions to return\r\nthe vehicle to the center of the lane.\r\n\r\n**Independent variables:** Background Audio (task relevant vs. non-task relevant vs. internal focus).\r\n\r\n**Dependent variables:** Reaction times to perturbations, continuous performance based on vehicle log\r\n(steering wheel angle, lane position, heading error, etc.), reaction times to target vehicles (police),\r\nTask-Induced Fatigue Scale (TIFS), Karolinska Sleepiness Scale (KSS), Visual Analog Scale of Fatigue (VAS-F).\r\nNote: 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 Location:** Teledyne Corporation, Durham, NC.\r\n\r\n**Note:** This dataset has a corresponding dataset in the BCIT Calibration Driving ds004118 which has the\r\n15 minute driving task prior to this one.\r\n","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":24961973504,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":null,"zarr_errors":60,"zarr_failure_count":60,"zarr_deterministic":1,"zarr_failed_at":"2026-08-05 21:44:57","num_dataset_citations":0,"num_datapaper_citations":32,"n_channels":64,"electrode_system":"10-10","has_hed":1,"hed_version":"8.0.0","is_exemplar":0,"bytes_present":25668845402,"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 16:27:34\",\"metadata_updated_at\":\"2026-08-19 16:27:46\",\"archive_checked_at\":\"2026-06-23 10:58:14\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-06-23 10:45:31\",\"citations_updated_at\":\"2026-09-08 03:00:48\",\"channel_montage_checked_at\":\"2026-06-28 23:20:23\",\"hed_checked_at\":\"2026-06-30 04:53:09\",\"data_checked_at\":\"2026-08-06 03:01:02\",\"availability_report_at\":\"2026-07-23 01:17:04\",\"recording_stats_at\":null,\"signal_defaults_at\":\"2026-09-02 12:08:05\"}","participants":21,"num_citations":32,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"nemarAdmin","owner_github":"nemarAdmin","file_size_formatted":"23.91 GB","zarr_data_failures":{"count":60,"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}}