DS004844: eeg dataset, 17 subjects#
T22
Citation: Jason S. Metcalfe, Victor Paul, Benamin Haynes, Corey Atwater, Amar Marathe, Gregory Gremillion, Kim Drnec, William Nothwang, Justin R. Estepp, Margaret Bowers, Jamie Lukos, Tony Johnson, Mike Dunkel, Stephen Gordon, Jon Touryan, Kevin King (—). T22. 10.18112/openneuro.ds004844.v1.0.0
17-participant EEG dataset — T22.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004844
dataset = DS004844(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004844(cache_dir="./data", subject="01")
Advanced query
dataset = DS004844(
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{ds004844,
title = {T22},
author = {Jason S. Metcalfe and Victor Paul and Benamin Haynes and Corey Atwater and Amar Marathe and Gregory Gremillion and Kim Drnec and William Nothwang and Justin R. Estepp and Margaret Bowers and Jamie Lukos and Tony Johnson and Mike Dunkel and Stephen Gordon and Jon Touryan and Kevin King},
doi = {10.18112/openneuro.ds004844.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004844.v1.0.0},
}
About This Dataset#
TX22 dataset: Predicting and influencing trust-based decisions about control authority hand-off and take-over during simulated, semi-automated driving in a leader-follower paradigm.Vehicle survivability is critically important in todays military. Significant DoD investments have focused on developing and integrating autonomous vehicle technologies to mitigate the effects of human error and thus enhance surviability and mission effectiveness. In a previous experiment (SANDR designation: ARL_TX20), we explored how a human operators acceptance and use of advanced technology is influenced by their trust and related factors, like subjective workload and automation reliability. Nevertheless, more critical than measuring and achieving a certain level of trust is the need for a capability to resolve observed (or predicted) discrepancies between trust and trustworthiness that will undermine effective joint system performance. Using the same paradigm as we developed for our previous experiment (ARL_TX20), here we explore our ability to (a) make accurate real-time predictions of instances where intervention is necessary and (b) use those predictions to provide feedback to the driver that is intended to support active “trust management” by influencing the trust-based decisions of the driver.
Cohort#
Dataset Statistics#
Channel counts: 72 ch (n=68 recordings)
Sampling frequencies: 1024.0 Hz (n=68 recordings)
Total recording duration: 21 h 15 min
Signal · Electrodes & live trace#
Live trace viewer — sub-010 · ses-CA · task-Drive · run-5
Showing one representative recording out of
17 subjects and 68 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 |
T22 |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Jason S. Metcalfe, Victor Paul, Benamin Haynes, Corey Atwater, Amar Marathe, Gregory Gremillion, Kim Drnec, William Nothwang, Justin R. Estepp, Margaret Bowers, Jamie Lukos, Tony Johnson, Mike Dunkel, Stephen Gordon, Jon Touryan, Kevin King |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004844,
title = {T22},
author = {Jason S. Metcalfe and Victor Paul and Benamin Haynes and Corey Atwater and Amar Marathe and Gregory Gremillion and Kim Drnec and William Nothwang and Justin R. Estepp and Margaret Bowers and Jamie Lukos and Tony Johnson and Mike Dunkel and Stephen Gordon and Jon Touryan and Kevin King},
doi = {10.18112/openneuro.ds004844.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004844.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004844 · Metcalfe2023_T22eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004844(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
T22
- Study:
ds004844(OpenNeuro)- Author (year):
Metcalfe2023_T22- Canonical:
—
Also importable as:
DS004844,Metcalfe2023_T22.Modality:
eeg; Experiment type:Decision-making; Subject type:Healthy. Subjects: 17; recordings: 68; 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/ds004844 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004844 DOI: https://doi.org/10.18112/openneuro.ds004844.v1.0.0 NEMAR citation count: 0
Examples
>>> from eegdash.dataset import DS004844 >>> dataset = DS004844(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/ds004844").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 ds004844 to reproduce the tutorial on this dataset.
Citation
Jason S. Metcalfe, Victor Paul, Benamin Haynes, Corey Atwater, Amar Marathe, … (n.d.). T22. 10.18112/openneuro.ds004844.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.ds004844.v1.0.0.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset