DS004121: eeg dataset, 21 subjects#
BCIT Mind Wandering
Citation: 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) (20). BCIT Mind Wandering. 10.18112/openneuro.ds004121.v1.0.0
21-participant EEG dataset — BCIT Mind Wandering.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004121
dataset = DS004121(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004121(cache_dir="./data", subject="01")
Advanced query
dataset = DS004121(
cache_dir="./data",
query={"subject": {"$in": ["01", "02"]}},
)
Iterate recordings
for rec in dataset:
print(rec.subject, rec.raw.info['sfreq'])
If you use this dataset in your research, please cite the original authors.
BibTeX
@dataset{ds004121,
title = {BCIT Mind Wandering},
author = {Jonathan Touryan (data and curation) and Greg Apker (data) and Brent Lance (data) and Scott Kerick (data) and Anthony Ries (data) and Justin Brooks (data) and Kaleb McDowell (data) and Tony Johnson (curation) and Kay Robbins (curation)},
doi = {10.18112/openneuro.ds004121.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004121.v1.0.0},
}
About This Dataset#
Overview: Subjects in the Mind Wandering study performed a long-duration simulated driving task
with perturbations and audio stimuli in a visually complex environment.
The purpose of this effort was to supplement and extend the related driving research to collect
prolonged time-on-task measurements of subjects performing a driving task in a simulated environment in order to assess fatigue-based performance through novel biomarkers. Similar to the Baseline Driving study, the Mind Wandring study was intended to identify periods of driver fatigue via predictive algorithms formulated from the analysis of driver EEG data, in comparison to the objective performance measures, and in contrast with the (non-fatigued) Calibration driving session for the subject.
BCIT Mind Wandering
Introduction
Mind Wandering extended the paradigm by adding different types of background audio (task relevant, non-task relevant, internal focus) and a vigilance task (identify police vehicles), in addition to increasing perturbation magnitude and frequency vs. baseline driving.
Further information is available on request from cancta.net.
View full README
BCIT Mind Wandering
Introduction
Mind Wandering extended the paradigm by adding different types of background audio (task relevant, non-task relevant, internal focus) and a vigilance task (identify police vehicles), in addition to increasing perturbation magnitude and frequency vs. baseline driving.
Further information is available on request from cancta.net.
Methods
Subjects: Volunteers from the local community recruited through advertisements. Apparatus: Driving simulator with steering wheel and brake / foot pedals (Real Time Technologies; Dearborn, MI); Video Refresh Rate (VRR) = 900 Hz; Vehicle data log file Sampling Rate (SR) = 100 Hz); EEG (BioSemi 64 (+8) channel systems with 4 eye and 2 mastoid channels recorded; SR=2048 Hz); Eye Tracking (Sensomotoric Instruments (SMI); REDEYE250). Initial setup: Upon arrival to the lab, subjects were given an introduction to the primary study for which they were recruited and provided informed consent and provided demographics information.
This was followed by a practice session, to acclimate the subject to the driving simulator. The driving practice task lasted 10-15 min, until asymptotic performance in steering and speed control was demonstrated and lack of motion sickness was reported.
Subjects were then outfitted and prepped for eye tracking and EEG acquisition. Task organization within the study: Subjects always began recording sessions by performing a Calibration Driving task, which was a 15-minute drive where the subject controlled only the steering (and speed was controlled by the simulator). Mind wandering task details: Subjects would perform Mind Wandering conditions A, B, and C, with counter-balancing used across subjects as to which of them came first.
Mind Wandering A was 30 minutes of continuous driving, with subjects responsible for steering and maintaining speed, while task relevant audio (traffic safety) played in the background.
Subjects were instructed to look for police vehicles and respond by pressing a button on the steering wheel. Mind Wandering B and C were similar, with non-task relevant audio (e.g. sports broadcast) in B and internal focus audio (mindfulness breathing exercise) in C.
Both driving tasks were conducted on the same simulated long, straight road, that contained a mix of regular traffic and police vehicles.
In each case, the subject was instructed to stay within the boundaries of the right-most lane, and to drive at the posted speed limits.
The vehicle was periodically subject to lateral perturbing forces, which could be applied to either side of the vehicle, pushing the vehicle out of the center of the lane; and the subject was instructed to execute corrective steering actions to return the vehicle to the center of the lane. Independent variables: Background Audio (task relevant vs. non-task relevant vs. internal focus). Dependent variables: Reaction times to perturbations, continuous performance based on vehicle log (steering wheel angle, lane position, heading error, etc.), reaction times to target vehicles (police), Task-Induced Fatigue Scale (TIFS), Karolinska Sleepiness Scale (KSS), Visual Analog Scale of Fatigue (VAS-F).
Note: questionnaire data is available upon request from cancta.net. Additional data acquired: Participant Enrollment Questionnaire, Subject Questionnaire for Current Session, Simulator Sickness Questionnaire. Experimental Location: Teledyne Corporation, Durham, NC. Note: This dataset has a corresponding dataset in the BCIT Calibration Driving ds004118 which has the 15 minute driving task prior to this one.
Cohort#
Dataset Statistics#
Channel counts: 74 ch (n=60 recordings)
Sampling frequencies: 1024.0 Hz (n=60 recordings)
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · ses-01 · task-DriveWithTaskAudio · run-2
Showing one representative recording out of
21 subjects and 60 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
Electrode layout — EEG · 64 sensors — 64 channels
NEMAR Processing Statistics#
The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.
HED event descriptors word cloud
Manifest#
File Explorer#
Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.
Full dataset metadata table
Dataset ID |
|
Title |
BCIT Mind Wandering |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
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 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004121,
title = {BCIT Mind Wandering},
author = {Jonathan Touryan (data and curation) and Greg Apker (data) and Brent Lance (data) and Scott Kerick (data) and Anthony Ries (data) and Justin Brooks (data) and Kaleb McDowell (data) and Tony Johnson (curation) and Kay Robbins (curation)},
doi = {10.18112/openneuro.ds004121.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004121.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004121 · Touryan2022_BCIT_Mindeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004121(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
BCIT Mind Wandering
- Study:
ds004121(OpenNeuro)- Author (year):
Touryan2022_BCIT_Mind- Canonical:
—
Also importable as:
DS004121,Touryan2022_BCIT_Mind.Modality:
eeg; Experiment type:Attention; Subject type:Healthy. Subjects: 21; recordings: 60; tasks: 1.- Parameters:
cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key
dataset.s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to
EEGDashDataset.
- data_dir#
Local dataset cache directory (
cache_dir / dataset_id).- Type:
Path
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/ds004121 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004121 DOI: https://doi.org/10.18112/openneuro.ds004121.v1.0.0 NEMAR citation count: 0
Examples
>>> from eegdash.dataset import DS004121 >>> dataset = DS004121(cache_dir="./data") >>> recording = dataset[0] >>> raw = recording.load()
- __init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
- save(path: str, overwrite: bool = False, offset: int = 0)[source]#
Save datasets to files by creating one subdirectory for each dataset:
path/ 0/ 0-raw.fif | 0-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw) 1/ 1-raw.fif | 1-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds004121").huggingface
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for ds004121 to reproduce the tutorial on this dataset.
Citation
Jonathan Touryan (data and curation), Greg Apker (data), Brent Lance (data), Scott Kerick (data), Anthony Ries (data), … (20). BCIT Mind Wandering. 10.18112/openneuro.ds004121.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004121.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset