DS004844: eeg dataset, 17 subjects#

T22

Access recordings and metadata through EEGDash.

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 (2023). T22. 10.18112/openneuro.ds004844.v1.0.0

Modality: eeg Subjects: 17 Recordings: 68 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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.

Dataset Information#

Dataset ID	`DS004844`
Title	T22
Author (year)	`Metcalfe2023_T22`
Canonical	—
Importable as	`DS004844`, `Metcalfe2023_T22`
Year	2023
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	doi:10.18112/openneuro.ds004844.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 17
Recordings: 68
Tasks: 1

Channels & sampling rate

Channels: 72
Sampling rate (Hz): 1024.0
Duration (hours): 21.252222222222223

Electrode Layout#

Electrode layout — EEG · 64 sensors — 64 channels

Dataset Statistics#

Channel counts: 72 ch (n=68 recordings)

Sampling frequencies: 1024.0 Hz (n=68 recordings)

Total recording duration: 21 h 15 min

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the DS004844 class to access this dataset programmatically.

class eegdash.dataset.DS004844(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

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

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and 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)