DS004625#

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: 3435 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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
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

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: 3435
Tasks: 1

Channels & sampling rate

Channels: 120 (543), 284 (323), 310 (187), 375 (33)
Sampling rate (Hz): 500.0
Duration (hours): 0.0

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

OpenNeuro dataset ds004625. Modality: eeg; Experiment type: Attention. 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

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