DS004841#

TX14

Access recordings and metadata through EEGDash.

Citation: Gabriella Larkin, James A. Davis, Victor Paul, Marcel Cannon, Chris Manteuffel, Ben Brewster, Tony Johnson, Mike Dunkel, Stephen Gordon, Kevin King (2023). TX14. 10.18112/openneuro.ds004841.v1.0.1

Modality: eeg Subjects: 20 Recordings: 1034 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004841

dataset = DS004841(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 = DS004841(cache_dir="./data", subject="01")

Advanced query

dataset = DS004841(
    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{ds004841,
  title = {TX14},
  author = {Gabriella Larkin and James A. Davis and Victor Paul and Marcel Cannon and Chris Manteuffel and Ben Brewster and Tony Johnson and Mike Dunkel and Stephen Gordon and Kevin King},
  doi = {10.18112/openneuro.ds004841.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004841.v1.0.1},
}

About This Dataset#

TX14 dataset: Perform a local situational awareness task while maintaining supervisory control of a semi-autonomous vehicle. This Army’s transition to a leaner, more agile and rapidly-deployable force requires the advent of autonomous technologies and systems, and more reliance on computers and machines. This move from traditional warfare to FCS represents a shift in the human role, as well. Technological advancement has made it so that the role of the user has been transformed from active controller to system monitor and manager, intervening only in the case of a problem. As such, the soldier’s dependency on robotics technologies, tele-operations, indirect driving and autonomy is expected to increase significantly. Additionally, although semi-autonomous driving technologies have proven beneficial in aggregate measures of local area awareness (i.e., target/threat detection) and vehicle control, it is important to understand the situational trade-offs between local area awareness and vehicle control, as situational trade-offs provide the basis for developing dynamic task allocation within Crewstations.

Dataset Information#

Dataset ID	`DS004841`
Title	TX14
Year	2023
Authors	Gabriella Larkin, James A. Davis, Victor Paul, Marcel Cannon, Chris Manteuffel, Ben Brewster, Tony Johnson, Mike Dunkel, Stephen Gordon, Kevin King
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004841.v1.0.1
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: 20
Recordings: 1034
Tasks: 1

Channels & sampling rate

Channels: 70 (147), 64 (147)
Sampling rate (Hz): 256.0
Duration (hours): 0.0

API Reference#

Use the DS004841 class to access this dataset programmatically.

class eegdash.dataset.DS004841(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004841. Modality: eeg; Experiment type: Attention. Subjects: 20; recordings: 147; 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/ds004841 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004841

Examples

>>> from eegdash.dataset import DS004841
>>> dataset = DS004841(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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None