DS004625: eeg dataset, 32 subjects#

Mind in Motion Young Adults Walking Over Uneven Terrain

Access recordings and metadata through EEGDash.

Citation: Chang Liu, Ryan J. Downey, Jacob S. Salminen, Sofia Arvelo Rojas, Erika M. Pliner, Natalie Richer, Jungyun Hwang, Yenisel Cruz-Almeida, Todd M. Manini, Chris J. Hass, Rachael D. Seidler, David J. Clark, Daniel P. Ferris (2023). Mind in Motion Young Adults Walking Over Uneven Terrain. 10.18112/openneuro.ds004625.v1.0.2

Modality: eeg Subjects: 32 Recordings: 543 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004625

dataset = DS004625(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004625(cache_dir="./data", subject="01")

Advanced query

dataset = DS004625(
    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{ds004625,
  title = {Mind in Motion Young Adults Walking Over Uneven Terrain},
  author = {Chang Liu and Ryan J. Downey and Jacob S. Salminen and Sofia Arvelo Rojas and Erika M. Pliner and Natalie Richer and Jungyun Hwang and Yenisel Cruz-Almeida and Todd M. Manini and Chris J. Hass and Rachael D. Seidler and David J. Clark and Daniel P. Ferris},
  doi = {10.18112/openneuro.ds004625.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds004625.v1.0.2},
}

About This Dataset#

Our dataset contains high-density, dual-layer electroencephalography (EEG), neck electromyography (EMG), inertial measurement unit (IMU) acceleration, ground reaction forces, head model constructed from T1 structural MR images from 32 participants walking over uneven terrain and at different speeds. Participants completed two trials for each condition for three minutes and a seated rest trial for three minutes. Digitized electrode locations (txt) are included in each subject folder. Please refer to our publication for more detail. This study was supported by the National Institute of Health (U01AG061389).

Dataset Information#

Dataset ID

DS004625

Title

Mind in Motion Young Adults Walking Over Uneven Terrain

Author (year)

Liu2023

Canonical

—

Importable as

DS004625, Liu2023

Year

2023

Authors

Chang Liu, Ryan J. Downey, Jacob S. Salminen, Sofia Arvelo Rojas, Erika M. Pliner, Natalie Richer, Jungyun Hwang, Yenisel Cruz-Almeida, Todd M. Manini, Chris J. Hass, Rachael D. Seidler, David J. Clark, Daniel P. Ferris

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004625.v1.0.2

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004625,
  title = {Mind in Motion Young Adults Walking Over Uneven Terrain},
  author = {Chang Liu and Ryan J. Downey and Jacob S. Salminen and Sofia Arvelo Rojas and Erika M. Pliner and Natalie Richer and Jungyun Hwang and Yenisel Cruz-Almeida and Todd M. Manini and Chris J. Hass and Rachael D. Seidler and David J. Clark and Daniel P. Ferris},
  doi = {10.18112/openneuro.ds004625.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds004625.v1.0.2},
}

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: 32

  • Recordings: 543

  • Tasks: 9

Channels & sampling rate

  • Channels: 284 (323), 310 (187), 375 (33)

  • Sampling rate (Hz): 500.0

  • Duration (hours): 28.581393888888886

Tags

  • Pathology: Healthy

  • Modality: Motor

  • Type: Motor

Files & format

  • Size on disk: 62.5 GB

  • File count: 543

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004625.v1.0.2

Provenance

API Reference#

Use the DS004625 class to access this dataset programmatically.

class eegdash.dataset.DS004625(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Mind in Motion Young Adults Walking Over Uneven Terrain

Study:

ds004625 (OpenNeuro)

Author (year):

Liu2023

Canonical:

—

Also importable as: DS004625, Liu2023.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 32; recordings: 543; tasks: 9.

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/ds004625 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004625 DOI: https://doi.org/10.18112/openneuro.ds004625.v1.0.2 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004625
>>> dataset = DS004625(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#