DS004033: eeg dataset, 18 subjects#
Electrode walking study
Citation: Joanna Scanlon, Nadine Jacobsen, Marike Maack, Stefan Debener (20). Electrode walking study. 10.18112/openneuro.ds004033.v1.0.0
18-participant EEG dataset — Electrode walking study.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004033
dataset = DS004033(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004033(cache_dir="./data", subject="01")
Advanced query
dataset = DS004033(
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{ds004033,
title = {Electrode walking study},
author = {Joanna Scanlon and Nadine Jacobsen and Marike Maack and Stefan Debener},
doi = {10.18112/openneuro.ds004033.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004033.v1.0.0},
}
About This Dataset#
The electrode and synchronized walking study: Participants performed 3 tasks outside:
eyes open/eyes close, standing, walking alone & walking with experimenter oddball task, and 3 walking with experimenter tasks Only part of the second task (standing and walking oddball) was used for the first paper.
Each of 18 participants performed the task using both active (session 1) and passive (session 2) electrodes, in counterbalanced order
Task 1: Eyes open / Eyes closed; 1. Participant stands facing a wall with eyes open (or closed) for 1 min.
Then 1 min of eyes closed (or open). This is counterbalanced and repeated. 2X each type. Task 2: Oddball task: Standing / Walking alone / Walking together. Participants performed an oddball task in which they listened to the tones through headphones and silently counted the deviant tones.
The tones were 800 and 1000 Hz, with the standard/target status counterbalanced across participants. During the walking conditions, participants walked clockwise around an outdoor (roofed) basketball arena, following pylons. Each block was about 5-6 minutes.
Blocks were counterbalanced and repeated 2X each. Task 3: Walking together: Natural / Control / Synchronize 3. Participants walked with the experimenter for 6 minutes in 3 conditions. The experimenter listened to a metronome while walking, and synchronized their steps with it (also true during walking together oddball task). During Natural walking, participant is just asked to walk with the experimenter, with no other instruction. In Control, participant is
blindedusingside-blinderswhich block their view of the experimenter. In Synchronize, participants try to synchronize their steps with the experimenter. All walking & oddball conditions started with a countdown (this has a specific trigger for oddball conds, not for task 3 conds. It plays during the first ~ 12 seconds of the 6 min trial - Joanna Scanlon (Feb 2022)
Cohort#
Dataset Statistics#
Age distribution by gender (n=18, range 20–28 yr, mean 24.0 yr)
Sex composition
Channel counts: 67 ch (n=36 recordings)
Sampling frequencies: 500.0 Hz (n=36 recordings)
Total recording duration: 42 h
Signal · Electrodes & live trace#
Live trace viewer — sub-010 · ses-02 · task-pas · run-2
Showing one representative recording out of
18 subjects and 36 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 · 67 sensors — 67 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 |
Electrode walking study |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Joanna Scanlon, Nadine Jacobsen, Marike Maack, Stefan Debener |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004033,
title = {Electrode walking study},
author = {Joanna Scanlon and Nadine Jacobsen and Marike Maack and Stefan Debener},
doi = {10.18112/openneuro.ds004033.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004033.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004033 · Scanlon2022eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004033(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Electrode walking study
- Study:
ds004033(OpenNeuro)- Author (year):
Scanlon2022- Canonical:
—
Also importable as:
DS004033,Scanlon2022.Modality:
eeg; Experiment type:Attention; Subject type:Healthy. Subjects: 18; recordings: 36; tasks: 2.- 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/ds004033 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004033 DOI: https://doi.org/10.18112/openneuro.ds004033.v1.0.0 NEMAR citation count: 2
Examples
>>> from eegdash.dataset import DS004033 >>> dataset = DS004033(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/ds004033").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 ds004033 to reproduce the tutorial on this dataset.
Citation
Joanna Scanlon, Nadine Jacobsen, Marike Maack, Stefan Debener (20). Electrode walking study. 10.18112/openneuro.ds004033.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004033.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset