DS004475#

Mobile EEG split-belt walking study

Access recordings and metadata through EEGDash.

Citation: Noelle A. Jacobsen, Daniel P. Ferris (2023). Mobile EEG split-belt walking study. 10.18112/openneuro.ds004475.v1.0.3

Modality: eeg Subjects: 30 Recordings: 246 License: CC0 Source: openneuro Citations: 2.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004475

dataset = DS004475(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004475(cache_dir="./data", subject="01")

Advanced query

dataset = DS004475(
    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{ds004475,
  title = {Mobile EEG split-belt walking study},
  author = {Noelle A. Jacobsen and Daniel P. Ferris},
  doi = {10.18112/openneuro.ds004475.v1.0.3},
  url = {https://doi.org/10.18112/openneuro.ds004475.v1.0.3},
}

About This Dataset#

This mobile brain body imaging (MoBI) experiment investigates brain activity correlated to gait adaptation during split-belt treadmill walking. 30 participants completed an abrupt and gradual split-belt walking paradigm (2:1 belt speed ratio).

Dataset Information#

Dataset ID

DS004475

Title

Mobile EEG split-belt walking study

Year

2023

Authors

Noelle A. Jacobsen, Daniel P. Ferris

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004475.v1.0.3

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004475,
  title = {Mobile EEG split-belt walking study},
  author = {Noelle A. Jacobsen and Daniel P. Ferris},
  doi = {10.18112/openneuro.ds004475.v1.0.3},
  url = {https://doi.org/10.18112/openneuro.ds004475.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: 246

  • Tasks: 1

Channels & sampling rate

  • Channels: 127 (6), 124 (6), 260 (5), 123 (4), 263 (3), 128 (3), 250 (3), 125 (3), 258 (3), 257 (3), 255 (3), 259 (2), 122 (2), 113, 254, 265, 252, 120, 126, 249, 256, 118, 119, 262, 261, 253, 115

  • Sampling rate (Hz): 512.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 48.5 GB

  • File count: 246

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004475.v1.0.3

Provenance

API Reference#

Use the DS004475 class to access this dataset programmatically.

class eegdash.dataset.DS004475(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004475. Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 30; recordings: 30; 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/ds004475 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004475

Examples

>>> from eegdash.dataset import DS004475
>>> dataset = DS004475(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#