DS003739#

Perturbed beam-walking task

Access recordings and metadata through EEGDash.

Citation: Steven Peterson, Daniel Ferris (2021). Perturbed beam-walking task. 10.18112/openneuro.ds003739.v1.0.3

Modality: eeg Subjects: 30 Recordings: 965 License: CC0 Source: openneuro Citations: 5.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003739

dataset = DS003739(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS003739(cache_dir="./data", subject="01")

Advanced query

dataset = DS003739(
    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{ds003739,
  title = {Perturbed beam-walking task},
  author = {Steven Peterson and Daniel Ferris},
  doi = {10.18112/openneuro.ds003739.v1.0.3},
  url = {https://doi.org/10.18112/openneuro.ds003739.v1.0.3},
}

About This Dataset#

Data was collected at the University of Michigan by Steven Peterson in the lab of Daniel Ferris. This study’s protocol was approved by the University of Michigan Institutional Review Board and all participants provided written consent. Please see our data publication for a more comprehensive description of this dataset: https://doi.org/10.1016/j.dib.2021.107635.

Each data file includes synchronized 128-channel EEG, lower leg EMG, neck EMG, EOG, and motion capture data. Participants performed four 10-minute, same-day sessions where they either stood or walked at 0.22 m/s on a treadmill-mounted balance beam that was 2.5 cm tall and 12.7 cm wide.

During each session, participants were exposed to sensorimotor perturbations (either virtual-reality-induced visual field rotations or side-to-side waist pulls, lasting 0.5 seconds and 1 second in duration, respectively). Each session involved 150 perturbation events, balanced between rotation/pull directions.

We have included the indices of all good channels for each participant in EEG.etc.good_chans of each .set file (includes non-EEG channel indices). Criteria for determining good/bad EEG channels can be found in our eNeuro publication. EEG.etc also includes the resulting ICA sphere and weight matrices when run on only the EEG channels, along with the selected good IC’s that were retained for our analyses.

Dataset Information#

Dataset ID

DS003739

Title

Perturbed beam-walking task

Year

2021

Authors

Steven Peterson, Daniel Ferris

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds003739.v1.0.3

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds003739,
  title = {Perturbed beam-walking task},
  author = {Steven Peterson and Daniel Ferris},
  doi = {10.18112/openneuro.ds003739.v1.0.3},
  url = {https://doi.org/10.18112/openneuro.ds003739.v1.0.3},
}

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: 30

  • Recordings: 965

  • Tasks: 1

Channels & sampling rate

  • Channels: 128 (120), 149 (120)

  • Sampling rate (Hz): 256.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 10.9 GB

  • File count: 965

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds003739.v1.0.3

Provenance

API Reference#

Use the DS003739 class to access this dataset programmatically.

class eegdash.dataset.DS003739(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003739. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 30; recordings: 120; tasks: 4.

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/ds003739 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003739

Examples

>>> from eegdash.dataset import DS003739
>>> dataset = DS003739(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

See Also#