DS001971#

Audiocue walking study

Access recordings and metadata through EEGDash.

Citation: Johanna Wagner, Ramon Martinez-Cancino, Scott Makeig, Arnaud Delorme, Christa Neuper, Teodoro Solis-Escalante, Gernot Mueller-Putz (2019). Audiocue walking study. 10.18112/openneuro.ds001971.v1.1.1

Modality: eeg Subjects: 20 Recordings: 1917 License: Creative commons Source: openneuro Citations: 2.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS001971

dataset = DS001971(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS001971(cache_dir="./data", subject="01")

Advanced query

dataset = DS001971(
    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{ds001971,
  title = {Audiocue walking study},
  author = {Johanna Wagner and Ramon Martinez-Cancino and Scott Makeig and Arnaud Delorme and Christa Neuper and Teodoro Solis-Escalante and Gernot Mueller-Putz},
  doi = {10.18112/openneuro.ds001971.v1.1.1},
  url = {https://doi.org/10.18112/openneuro.ds001971.v1.1.1},
}

About This Dataset#

This mobile brain body imaging (MoBI) gait adaptation experiment contains 18 subjects. Participants were walking on a treadmill at a constant speed and were required to step in time to an auditory tone sequence and adapt their step length and rate to occasional shifts in tempo of the pacing stimulus (i.e., following shifts to a faster or slower tempo). The scientific article (see Reference) contains all methodological details

  • Johanna Wagner (June 6, 2019)

Dataset Information#

Dataset ID

DS001971

Title

Audiocue walking study

Year

2019

Authors

Johanna Wagner, Ramon Martinez-Cancino, Scott Makeig, Arnaud Delorme, Christa Neuper, Teodoro Solis-Escalante, Gernot Mueller-Putz

License

Creative commons

Citation / DOI

10.18112/openneuro.ds001971.v1.1.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds001971,
  title = {Audiocue walking study},
  author = {Johanna Wagner and Ramon Martinez-Cancino and Scott Makeig and Arnaud Delorme and Christa Neuper and Teodoro Solis-Escalante and Gernot Mueller-Putz},
  doi = {10.18112/openneuro.ds001971.v1.1.1},
  url = {https://doi.org/10.18112/openneuro.ds001971.v1.1.1},
}

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: 1917

  • Tasks: 1

Channels & sampling rate

  • Channels: 108 (273), 115 (206), 112 (67)

  • Sampling rate (Hz): 512.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 32.0 GB

  • File count: 1917

  • Format: BIDS

License & citation

  • License: Creative commons

  • DOI: 10.18112/openneuro.ds001971.v1.1.1

Provenance

API Reference#

Use the DS001971 class to access this dataset programmatically.

class eegdash.dataset.DS001971(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds001971. Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 20; recordings: 273; 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/ds001971 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds001971

Examples

>>> from eegdash.dataset import DS001971
>>> dataset = DS001971(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#