{"dataset":{"id":"59269","dataset_id":"nm000275","name":"Multi-channel EEG recordings during a sustained-attention driving task","description":"This dataset comprises multi-channel EEG recordings from 27 healthy adults performing a sustained-attention driving task in a virtual-reality simulator across 62 sessions. The task involved event-related lane-departure paradigms where participants maintained vehicle position on a simulated highway, with recordings capturing 32-channel EEG (30 scalp + 2 mastoid references) sampled at 500 Hz alongside vehicle position data. The dataset includes approximately 82 hours of raw, unfiltered EEG data with over 27,000 lane-departure trials and associated behavioral markers, providing a resource for investigating fatigue, drowsiness, and sustained attention mechanisms.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000275","concept_doi":"10.82901/nemar.nm000275","latest_version_doi":"10.82901/nemar.nm000275.v1.0.0","created_at":"2026-06-25 18:21:31","updated_at":"2026-07-10 22:04:42","zenodo_concept_id":"21124685","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Multi-channel EEG recordings during a sustained-attention driving task\",\n  \"description\": \"This dataset comprises multi-channel EEG recordings from 27 healthy adults performing a sustained-attention driving task in a virtual-reality simulator across 62 sessions. The task involved event-related lane-departure paradigms where participants maintained vehicle position on a simulated highway, with recordings capturing 32-channel EEG (30 scalp + 2 mastoid references) sampled at 500 Hz alongside vehicle position data. The dataset includes approximately 82 hours of raw, unfiltered EEG data with over 27,000 lane-departure trials and associated behavioral markers, providing a resource for investigating fatigue, drowsiness, and sustained attention mechanisms.\",\n  \"methods_description\": \"EEG was acquired using a Compumedics Neuroscan Scan SynAmps2 Express amplifier with a 32-channel Quik-Cap (Ag/AgCl electrodes in modified 10-20 configuration) at 500 Hz sampling rate with 16-bit quantization. Electrode-skin impedance was maintained below 5 kOhm. Participants performed a virtual-reality driving task on a six-degree-of-freedom Stewart motion platform with a real Ford Probe car frame, driving a simulated night-time highway at constant speed while maintaining lane position. Lane-departure events were randomly induced left or right, requiring steering responses. Sessions lasted 41-118 minutes (median ~70 minutes) between 2005-2012.\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Zehong Cao\": {\n      \"orcid\": \"0000-0003-3656-0328\"\n    },\n    \"Chun-Hsiang Chuang\": {},\n    \"Jung-Kai King\": {},\n    \"Chin-Teng Lin\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"sustained attention\"\n    },\n    {\n      \"term\": \"driving task\"\n    },\n    {\n      \"term\": \"fatigue detection\"\n    },\n    {\n      \"term\": \"reaction time\"\n    },\n    {\n      \"term\": \"virtual reality\"\n    },\n    {\n      \"term\": \"event-related potentials\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.6084/m9.figshare.6427334.v5\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"10.1038/s41597-019-0027-4\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.6084/m9.figshare.7666055.v3\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1016/j.neuroimage.2014.01.015\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1038/srep21353\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1109/TBCAS.2014.2316224\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1109/TNNLS.2013.2275003\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1016/j.knosys.2015.01.007\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1109/TNNLS.2015.2496330\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1109/TFUZZ.2016.2633379\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000275\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000275\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"U.S. Army Research Laboratory\",\n      \"award_number\": \"W911NF-10-2-0022\"\n    },\n    {\n      \"funder_name\": \"U.S. Army Research Laboratory\",\n      \"award_number\": \"W911NF-10-D-0002/TO 0023\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"39.0 GB (127 files)\"\n  ],\n  \"formats\": [\n    \".json\",\n    \".log\",\n    \".md\",\n    \".pdf\",\n    \".py\",\n    \".set\",\n    \".sh\",\n    \".tsv\",\n    \".txt\",\n    \".xml\",\n    \".yml\",\n    \".zip\"\n  ],\n  \"source_hash\": \"d29a768de50ab6007255179480a30fe001af1e6cd6e041545609b66bb9341366\"\n}","last_activity_at":"2026-06-25 20:09:49","source":null,"source_id":null,"subject_count":27,"modalities":"eeg","age_min":null,"age_max":null,"file_size":39030516359,"total_files":127,"tasks":"driving","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Zehong Cao, Chun-Hsiang Chuang, Jung-Kai King, Chin-Teng Lin","license":"CC-BY-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000275-blue)](https://doi.org/10.82901/nemar.nm000275)\n\n# Multi-channel EEG recordings during a sustained-attention driving task\n\nBIDS-EEG conversion of the **raw** dataset from:\n\n> Cao, Z., Chuang, C.-H., King, J.-K. & Lin, C.-T. (2019).\n> Multi-channel EEG recordings during a sustained-attention driving task.\n> *Scientific Data* 6, 19. https://doi.org/10.1038/s41597-019-0027-4\n\nOriginal data (CC BY 4.0): figshare https://doi.org/10.6084/m9.figshare.6427334.v5 (raw),\nhttps://doi.org/10.6084/m9.figshare.7666055.v3 (pre-processed). Open-access full text: PMC6472414.\n\n## Dataset at a glance\n\n| Property | Value |\n|---|---|\n| Modality | EEG (`eeg`) |\n| Task | `driving` (event-related lane-departure, sustained attention) |\n| Participants | 27 healthy adults (aged 22-28) |\n| Sessions (recordings) | 62 |\n| Total recording time | ~82 hours (per-session ~41-118 min) |\n| Channels | 32 EEG (30 scalp + A1/A2 mastoid refs) + 1 `vehicle_position` (misc) |\n| Sampling rate | 500 Hz, 16-bit |\n| Lane-departure (deviation) trials | 27192 total |\n| Event markers | 251/252 deviation L/R, 253 response onset, 254 response offset (+ undocumented 255 in 2 recordings) |\n| Data format | EEGLAB `.set` (BIDS-EEG) |\n| Electrode positions | nominal 10-20 template (not individually measured) |\n| License | CC BY 4.0 |\n| BIDS version | 1.9.0 |\n\n## Authors and affiliations\n\n1. Zehong Cao (ORCID 0000-0003-3656-0328) - Discipline of ICT, School of Technology, Environments and Design, University of Tasmania, Hobart, TAS, Australia\n2. Chun-Hsiang Chuang - Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan\n3. Jung-Kai King - Brain Research Center, National Chiao Tung University, Hsinchu, Taiwan\n4. Chin-Teng Lin - Centre for Artificial Intelligence, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW, Australia\n\n## Overview\n\n27 participants (students/staff of National Chiao Tung University, aged\n22-28, normal or corrected-to-normal vision, all holding a valid driver's\nlicence and with no history of psychological/neurological disorders or drug\nuse) performed a sustained-attention driving task (designed for ~90 minutes),\nat one or more sessions on the same or different days, yielding **62 EEG\nsessions**. Actual recording durations vary\n(~41-118 min; median ~70 min).\nRecordings were collected between 2005 and 2012. Participants received ~USD $20\nper session and completed a pre-test session to rule out simulator sickness.\n\n### Task: event-related lane-departure paradigm\n\nA virtual-reality (VR) dynamic driving simulator (built with WorldToolKit R9 and\nVisual C++) was mounted on a six-degree-of-freedom Stewart motion platform, with\na real Ford Probe car frame and six projected scenes giving a near-360-degree\nfield of view. Participants drove a visually monotonous night-time straight\nfour-lane divided highway with no other traffic at a constant 100 km/h, keeping\nthe car centred in the third lane. Road position was quantised to 0-255 (lane\nwidth 60 units). Lane-departure events were randomly induced with equal\nprobability to the left or right (**deviation onset**); the participant\ncounter-steered (**response onset**) to bring the car back to the lane centre\n(**response offset**), using the steering wheel only (no accelerator/brake). The\nnext trial began 5-10 s after the previous one. The interval from deviation\nonset to response onset is the **reaction time (RT)**, an index of fatigue and\ndrowsiness. The task was designed to run ~90 minutes without breaks; the actual\nrecorded duration varies by session (see the glance table and each\n`sub-XX_sessions.tsv`).\n\n## Acquisition\n\n- Amplifier: Compumedics Neuroscan **Scan SynAmps2 Express** system (Compumedics\n  Ltd., VIC, Australia). NOTE: the source paper inconsistently also refers to a\n  \"Scan NuAmps Express\" system in one section.\n- Cap: 32-channel **Quik-Cap** (Compumedics NeuroScan), Ag/AgCl electrodes.\n- Acquisition software: Neuroscan Scan 4.5 (raw saved originally as .cnt).\n- Sampling rate: 500 Hz; 16-bit quantisation.\n- Electrodes: 30 scalp EEG electrodes (modified international 10-20 system) plus\n  2 mastoid reference electrodes (A1, A2). Electrode-skin impedance kept < 5 kOhm\n  (NaCl conductive Quik-Gel; Nuprep + 70% isopropyl-alcohol skin prep).\n- Power-line frequency: 60 Hz (Taiwan).\n- Channel order (32 EEG): FP1, FP2, F7, F3, FZ, F4, F8, FT7, FC3, FCZ, FC4, FT8, T3, C3, CZ, C4, T4, TP7, CP3, CPZ, CP4, TP8, A1, T5, P3, PZ, P4, T6, A2, O1, OZ, O2.\n  Classic names map to modern ones as T3/T4/T5/T6 = T7/T8/P7/P8.\n- A 33rd channel, `vehicle_position` (BIDS type `misc`), stores the simulated\n  car's lateral position (quantised 0-255) sampled with the EEG at 500 Hz. In\n  the source files it is literally named \"vehicle position\"; the space was\n  replaced by an underscore for BIDS.\n\n### Electrode positions\n\nThe source recordings did **not** include measured electrode coordinates. For\nconvenience, each recording carries **nominal 10-20 template positions** (MNE\n`standard_1020`) under the original channel names, written to\n`*_electrodes.tsv` / `*_coordsystem.json`. These are idealised, identical across\nall recordings, and are **not** individually measured - treat them as\napproximate only.\n\n## Events\n\nEach `*_events.tsv` has `trial_type` (descriptive label) and `value` (original\ninteger trigger):\n\n| value | trial_type             | meaning                                          |\n|-------|------------------------|--------------------------------------------------|\n| 251   | deviation_onset_left   | car drift induced toward the left                |\n| 252   | deviation_onset_right  | car drift induced toward the right               |\n| 253   | response_onset         | subject starts steering back toward lane centre  |\n| 254   | response_offset        | subject finishes steering; car back at centre    |\n| 255   | undocumented_255       | extra trigger in sub-54 & sub-55 only (21x); not defined in the source publication, meaning unknown |\n\n## Subjects and sessions\n\nSubject labels preserve the original numbering (`sub-01` = original `s01`), so\nnumbering is non-consecutive. Sessions `ses-01`, `ses-02`, ... are ordered\nchronologically per subject. `sub-XX/sub-XX_sessions.tsv` maps each session to\nthe original recording file name, acquisition date, and the original one-letter\nfilename suffix (`m`/`n`) - an opaque label from the source naming whose meaning\nis not defined in the original publication.\n\n## Relationship to the pre-processed dataset\n\nA separately published pre-processed version (https://doi.org/10.6084/m9.figshare.7666055.v3) applied a 1-Hz\nhigh-pass and 50-Hz low-pass FIR filter and removed ocular/muscular artefacts\n(manual eye-blink rejection + the EEGLAB AAR plug-in). **This** BIDS dataset\ncontains the **raw**, unfiltered recordings.\n\n## Related publications using this dataset\n\n- https://doi.org/10.1016/j.neuroimage.2014.01.015\n- https://doi.org/10.1038/srep21353\n- https://doi.org/10.1109/TBCAS.2014.2316224\n- https://doi.org/10.1109/TNNLS.2013.2275003\n- https://doi.org/10.1016/j.knosys.2015.01.007\n- https://doi.org/10.1109/TNNLS.2015.2496330\n- https://doi.org/10.1109/TFUZZ.2016.2633379\n\n## Loading the data (Python / MNE)\n\n```python\nimport mne\nfrom mne_bids import BIDSPath, read_raw_bids\nbp = BIDSPath(subject=\"01\", session=\"01\", task=\"driving\",\n              datatype=\"eeg\", root=\"/path/to/this/dataset\")\nraw = read_raw_bids(bp)          # mne Raw with events as annotations\nprint(raw.info)                  # 32 EEG + vehicle_position (misc), 500 Hz\nevents, event_id = mne.events_from_annotations(raw)\n```\n\nThe `vehicle_position` channel (type `misc`) holds the simulated car's lateral\nposition (quantised 0-255) and can be epoched alongside the EEG to recover the\nsteering trajectory and verify reaction times.\n\n## Validation\n\nThis dataset passes the official `bids-validator` (Deno schema validator,\nv1.15.0+) with **0 errors**. One non-blocking warning remains\n(`MISSING_SESSION`): participants intentionally have different numbers of\nsessions (1-5), which is an inherent property of the original study design, not","bids_version":"1.9.0","sessions_count":5,"publish_date":null,"embedding_dirty":0,"license_tier":"attribution","zarr_status":"ready","zarr_converted_at":"2026-08-27 09:59:22","zarr_store_count":62,"zarr_index_etag":"0c2f70ac70ec1ee0478865ef1dd0d551","zarr_source_commit":"a1774663f3ac42ca5e784b85dfa1b609a04427bb","archive_status":"ready","archive_size":19382291601,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":null,"zarr_errors":0,"zarr_failure_count":0,"zarr_deterministic":0,"zarr_failed_at":null,"num_dataset_citations":0,"num_datapaper_citations":68,"n_channels":32,"electrode_system":"10-10","has_hed":1,"hed_version":"8.2.0","is_exemplar":0,"bytes_present":null,"data_complete":null,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":null,"archive_absent_files":null,"archive_declared_files":null,"zarr_pool_breaks":0,"total_recording_duration":295058.5199999999,"recording_duration_min":2461.04,"recording_duration_max":7061.04,"recording_count":62,"recordings_unavailable":0,"recordings_measured":62,"channel_count_min":33,"channel_count_max":33,"sampling_frequency":500,"power_line_frequency":60,"eeg_reference":"Mastoid reference electrodes A1 and A2 placed on opposite lateral mastoid bones (both recorded as data channels).","placement_scheme":"Modified international 10-20 system: 30 scalp electrodes plus mastoid references A1, A2 (classic nomenclature; T3/T4/T5/T6 = T7/T8/P7/P8).","sweep_stamps":"{\"enrichment_updated_at\":\"2026-07-02 04:26:51\",\"metadata_updated_at\":\"2026-07-02 04:27:25\",\"archive_checked_at\":\"2026-07-02 04:42:20\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-07-02 04:28:26\",\"citations_updated_at\":\"2026-09-01 03:00:30\",\"channel_montage_checked_at\":\"2026-06-28 23:03:04\",\"hed_checked_at\":\"2026-06-30 04:33:18\",\"data_checked_at\":null,\"availability_report_at\":\"2026-07-23 01:10:24\",\"recording_stats_at\":\"2026-09-02 11:32:08\",\"signal_defaults_at\":\"2026-09-02 11:51:05\"}","participants":27,"num_citations":68,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"bruaristimunha","owner_github":"bruAristimunha","file_size_formatted":"36.35 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/nm000275/zarr/index.json","attestation_deposit_type":null,"attestation_key_status":null,"attestation_deidentified":null,"attestation_no_duplicate":null,"attestation_upstream_source":null,"attestation_accepted_at":null}}