DS003739: eeg dataset, 30 subjects#
Perturbed beam-walking task
Citation: Steven Peterson, Daniel Ferris (—). Perturbed beam-walking task. 10.18112/openneuro.ds003739.v1.0.2
30-participant EEG dataset — Perturbed beam-walking task.
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.v1.0.2},
url = {https://doi.org/10.18112/openneuro.ds003739.v1.0.2},
}
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.
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.
Cohort#
Dataset Statistics#
Age distribution by gender (n=30, range 18–34 yr, mean 22.5 yr)
Sex composition
Channel counts: 149 ch (n=120 recordings)
Sampling frequencies: 256.0 Hz (n=120 recordings)
Total recording duration: 20 h 34 min
Signal · Electrodes & live trace#
Live trace viewer — sub-021 · ses-02 · task-pullwalk
Showing one representative recording out of
30 subjects and 120 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
Electrode layout — EEG · 133 sensors — 133 channels
NEMAR Processing Statistics#
The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.
HED event descriptors word cloud
Manifest#
File Explorer#
Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.
Full dataset metadata table
Dataset ID |
|
Title |
Perturbed beam-walking task |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Steven Peterson, Daniel Ferris |
License |
CC0 |
Citation / DOI |
|
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.v1.0.2},
url = {https://doi.org/10.18112/openneuro.ds003739.v1.0.2},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003739 · Peterson2021_Perturbed_beam_walkingeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003739(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Perturbed beam-walking task
- Study:
ds003739(OpenNeuro)- Author (year):
Peterson2021_Perturbed_beam_walking- Canonical:
—
Also importable as:
DS003739,Peterson2021_Perturbed_beam_walking.Modality:
eeg. 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand 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 DOI: https://doi.org/10.18112/openneuro.ds003739.v1.0.2 NEMAR citation count: 5
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: str, overwrite: bool = False, offset: int = 0)[source]#
Save datasets to files by creating one subdirectory for each dataset:
path/ 0/ 0-raw.fif | 0-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw) 1/ 1-raw.fif | 1-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds003739").huggingface
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for ds003739 to reproduce the tutorial on this dataset.
Citation
Steven Peterson, Daniel Ferris (n.d.). Perturbed beam-walking task. 10.18112/openneuro.ds003739.v1.0.2
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds003739.v1.0.2.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset