DS006095: eeg dataset, 71 subjects#
Mind in Motion Older Adults Walking Over Uneven Terrain
Citation: Chang Liu, Erika M. Pliner, Jacob S. Salminen, Ryan Downey, Jungyun Hwang, Akraprava Roy, Ryland Swearinger, Natalie Richer, Chris J. Hass, David J. Clark, Todd M. Manini, Yenisel Cruz-Almeida, Rachael D. Seidler, Daniel P. Ferris (—). Mind in Motion Older Adults Walking Over Uneven Terrain. 10.18112/openneuro.ds006095.v1.0.0
71-participant EEG dataset — Mind in Motion Older Adults Walking Over Uneven Terrain.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS006095
dataset = DS006095(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS006095(cache_dir="./data", subject="01")
Advanced query
dataset = DS006095(
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{ds006095,
title = {Mind in Motion Older Adults Walking Over Uneven Terrain},
author = {Chang Liu and Erika M. Pliner and Jacob S. Salminen and Ryan Downey and Jungyun Hwang and Akraprava Roy and Ryland Swearinger and Natalie Richer and Chris J. Hass and David J. Clark and Todd M. Manini and Yenisel Cruz-Almeida and Rachael D. Seidler and Daniel P. Ferris},
doi = {10.18112/openneuro.ds006095.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds006095.v1.0.0},
}
About This Dataset#
Our dataset contains high-density, dual-layer electroencephalography (EEG), neck electromyography (EMG), inertial measurement unit (IMU) acceleration, ground reaction force from all 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. Please refer to our publication for more detail. Digitized electrode locations (txt) are included in each subject folder.
Cohort#
Dataset Statistics#
Age distribution by gender (n=71, range 64–90 yr, mean 74.7 yr)
Sex composition
Channel counts (ch)
Sampling frequencies: 500.0 Hz (n=1182 recordings)
Total recording duration: 61 h
Signal · Electrodes & live trace#
Live trace viewer — sub-021 · task-low · run-2
Showing one representative recording out of
71 subjects and 1182 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 · 120 sensors — 120 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 |
Mind in Motion Older Adults Walking Over Uneven Terrain |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Chang Liu, Erika M. Pliner, Jacob S. Salminen, Ryan Downey, Jungyun Hwang, Akraprava Roy, Ryland Swearinger, Natalie Richer, Chris J. Hass, David J. Clark, Todd M. Manini, Yenisel Cruz-Almeida, Rachael D. Seidler, Daniel P. Ferris |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds006095,
title = {Mind in Motion Older Adults Walking Over Uneven Terrain},
author = {Chang Liu and Erika M. Pliner and Jacob S. Salminen and Ryan Downey and Jungyun Hwang and Akraprava Roy and Ryland Swearinger and Natalie Richer and Chris J. Hass and David J. Clark and Todd M. Manini and Yenisel Cruz-Almeida and Rachael D. Seidler and Daniel P. Ferris},
doi = {10.18112/openneuro.ds006095.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds006095.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS006095 · Liu2025_Mind_Motion_Oldereegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS006095(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Mind in Motion Older Adults Walking Over Uneven Terrain
- Study:
ds006095(OpenNeuro)- Author (year):
Liu2025_Mind_Motion_Older- Canonical:
—
Also importable as:
DS006095,Liu2025_Mind_Motion_Older.Modality:
eeg; Experiment type:Motor; Subject type:Healthy. Subjects: 71; recordings: 1182; 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
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/ds006095 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006095 DOI: https://doi.org/10.18112/openneuro.ds006095.v1.0.0
Examples
>>> from eegdash.dataset import DS006095 >>> dataset = DS006095(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/ds006095").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 ds006095 to reproduce the tutorial on this dataset.
Citation
Chang Liu, Erika M. Pliner, Jacob S. Salminen, Ryan Downey, Jungyun Hwang, … (n.d.). Mind in Motion Older Adults Walking Over Uneven Terrain. 10.18112/openneuro.ds006095.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.ds006095.v1.0.0.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset