DS001971: eeg dataset, 20 subjects#
Audiocue walking study
Citation: Johanna Wagner, Ramon Martinez-Cancino, Scott Makeig, Arnaud Delorme, Christa Neuper, Teodoro Solis-Escalante, Gernot Mueller-Putz (20). Audiocue walking study. 10.18112/openneuro.ds001971.v1.1.1
20-participant EEG dataset — Audiocue walking study.
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)
Cohort#
Dataset Statistics#
Age distribution by gender (n=5, range 23–38 yr, mean 29.8 yr)
Sex composition
Channel counts (ch)
Sampling frequencies: 512.0 Hz (n=273 recordings)
Total recording duration: 39 h
Signal · Electrodes & live trace#
Live trace viewer — sub-019 · task-AudioCueWalkingStudy · run-16
Showing one representative recording out of
20 subjects and 273 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 · 108 sensors — 108 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 |
Audiocue walking study |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Johanna Wagner, Ramon Martinez-Cancino, Scott Makeig, Arnaud Delorme, Christa Neuper, Teodoro Solis-Escalante, Gernot Mueller-Putz |
License |
Creative commons |
Citation / DOI |
|
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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS001971 · Wagner2019eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS001971(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Audiocue walking study
- Study:
ds001971(OpenNeuro)- Author (year):
Wagner2019- Canonical:
—
Also importable as:
DS001971,Wagner2019.Modality:
eeg. 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
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/ds001971 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds001971 DOI: https://doi.org/10.18112/openneuro.ds001971.v1.1.1 NEMAR citation count: 2
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: 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/ds001971").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 ds001971 to reproduce the tutorial on this dataset.
Citation
Johanna Wagner, Ramon Martinez-Cancino, Scott Makeig, Arnaud Delorme, Christa Neuper, … (20). Audiocue walking study. 10.18112/openneuro.ds001971.v1.1.1
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds001971.v1.1.1.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset