DS006945: eeg dataset, 5 subjects#
Dataset: T1-Weighted Structural MRI and fMRI of Participants Viewing Self-Avatar Exoskeleton Walking (11 SWS Cycles)
Citation: Shantanu Sarkar, Kevin Nathan, Jose L. Contreras-Vidal (—). Dataset: T1-Weighted Structural MRI and fMRI of Participants Viewing Self-Avatar Exoskeleton Walking (11 SWS Cycles). 10.18112/openneuro.ds006945.v1.2.1
5-participant EEG dataset — Dataset: T1-Weighted Structural MRI and fMRI of Participants Viewing Self-Avatar Exoskeleton Walking (11 SWS Cycles).
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS006945
dataset = DS006945(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS006945(cache_dir="./data", subject="01")
Advanced query
dataset = DS006945(
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{ds006945,
title = {Dataset: T1-Weighted Structural MRI and fMRI of Participants Viewing Self-Avatar Exoskeleton Walking (11 SWS Cycles)},
author = {Shantanu Sarkar and Kevin Nathan and Jose L. Contreras-Vidal},
doi = {10.18112/openneuro.ds006945.v1.2.1},
url = {https://doi.org/10.18112/openneuro.ds006945.v1.2.1},
}
About This Dataset#
Neuroimaging Data Collected During Kinesthetic Motor Imagery of Walking vs. Rest
This dataset includes multimodal neuroimaging recordings from five participants performing kinesthetic motor imagery (KI) while viewing themselves walking in an exoskeleton. The dataset includes synchronized MRI (structural and functional) and EEG recordings organized according to the BIDS specification. Functional MRI data were acquired in two runs while participants viewed a 10-minute video, along with a separate baseline scan during which participants simulated a resting state for approximately 5 minutes. MRI sessions were conducted after participants completed nine sessions of EEG‑controlled exoskeleton walking and standing experiments.
Dataset link: <a href=”https://openneuro.org/datasets/ds006940” target=”_blank”> https://openneuro.org/datasets/ds006940 </a>
MRI Acquisition: - Scanner: Philips Ingenia 3.0T (Koninklijke Philips N.V., The Netherlands) - Structural scans: T1‑weighted anatomical images - Functional scans (fMRI): Participants viewed a 10‑minute video of themselves walking in the exoskeleton, filmed from a first‑person perspective. The video contained 11 Stop‑Walk‑Stop (SWS) cycles. During viewing, participants were instructed to evoke KI in synchrony with the exoskeleton movements. - Baseline condition: Participants mentally simulated resting state for approximately 5 minutes while fMRI data was recorded.
EEG Acquisition: - MR‑compatible EEG cap (Brain Products GmbH, Gilching, Germany) - Electrode locations are provided in EEGLAB format. - 59 scalp channels + 4 EOG channels + 1 ECG channel
Stimuli: - A video stimulus (
stimuli/walking_exoskeleton_S1.mp4) was presented during walking tasks.Participants:
View full README
EEG Acquisition: - MR‑compatible EEG cap (Brain Products GmbH, Gilching, Germany) - Electrode locations are provided in EEGLAB format. - 59 scalp channels + 4 EOG channels + 1 ECG channel
Stimuli: - A video stimulus (
stimuli/walking_exoskeleton_S1.mp4) was presented during walking tasks.Participants:
Five healthy adults out of seven participated in the EEG‑controlled exoskeleton experiments.
Participants S6 and S7 did not undergo MRI scanning due to a pause in data collection during the COVID‑19 pandemic. <h3>Folder Structure (Example: Participant S1)</h3> <hr> <pre>
├── dataset_description.json ├── README ├── derivatives │ └── sub-01 │ └── ses-01 │ ├── anat │ │ └── sub-01_ses-01_T1w.nii │ ├── dwi │ │ ├── sub-01_ses-01_run-001_dwi.json │ │ ├── sub-01_ses-01_run-001_dwi.bval │ │ ├── sub-01_ses-01_run-001_dwi.bvec │ │ └── sub-01_ses-01_run-001_dwi.nii.gz │ │ │ └── spm │ ├── sub-01_ses-01_beta_0001.nii │ ├── ... │ ├── sub-01_ses-01_beta_0008.nii │ ├── sub-01_ses-01_con_0001.nii │ ├── ... │ ├── sub-01_ses-01_con_0004.nii │ ├── sub-01_ses-01_smpt_0001.nii │ ├── ... │ ├── sub-01_ses-01_smpt_0004.nii │ ├── sub-01_ses-01_mask.mat │ ├── sub-01_ses-01_resms.mat │ ├── sub-01_ses-01_rpv.mat │ └── sub-01_ses-01_spm.mat │ ├── stimuli │ └── walking_exoskeleton_S1.mp4 │ ├── sub-01 │ └── ses-01 │ ├── anat │ │ ├── sub-01_ses-01_T1w.json │ │ └── sub-01_ses-01_T1w.nii │ ├── eeg │ │ ├── sub-01_ses-01_coordsystem.json │ │ ├── sub-01_ses-01_electrodes.json │ │ ├── sub-01_ses-01_electrodes.tsv │ │ ├── sub-01_ses-01_task-baseline_eeg.eeg │ │ ├── sub-01_ses-01_task-baseline_eeg.json │ │ ├── sub-01_ses-01_task-baseline_eeg.vhdr │ │ ├── sub-01_ses-01_task-baseline_eeg.vmrk │ │ ├── sub-01_ses-01_task-walking1_eeg.eeg │ │ ├── ... │ │ └── sub-01_ses-01_task-walking2_eeg.vmrk │ │ │ └── func │ ├── sub-01_ses-01_task-baseline_run-001_bold.json │ ├── sub-01_ses-01_task-baseline_run-001_bold.nii.gz │ ├── sub-01_ses-01_task-walking1_run-001_bold.json │ ├── sub-01_ses-01_task-walking1_run-001_bold.nii.gz │ ├── sub-01_ses-01_task-walking2_run-001_bold.json │ └── sub-01_ses-01_task-walking2_run-001_bold.nii.gz</pre>
Validation Data
A validation file (
derivatives/MRI_DataValidation.xls) is provided to summarize dataset completeness and quality checks. - Sheet: FilesLists presence/absence of EEG, MRI, and SPM outputs across subjects (S1–S5). Includes counts for beta, con, spmT maps, and DTI volumes.
Sheet: VMRK-R128 Reports event marker counts (R128 triggers) for baseline, walking1, and walking2 tasks.
Sheet: EEG-Duration Provides task durations (minutes) for ‘baseline’, ‘walking1’, and ‘walking2’ EEG recordings.
Notes on Organization
* Raw data (anat, func, eeg) are stored under each subject directory (sub-XX/ses-YY). * Derivatives: Preprocessed outputs are stored separately under derivatives/sub-XX/ses-YY, including:
 - Statistical Parametric Mapping (SPM) outputs
 - SPM-normalized (warped) anatomical scans
 - Diffusion Tensor Imaging (DTI) derivatives
 - Validation Excel file
* The video stimulus is stored in the top-level stimuli/ folder. * Naming conventions follow BIDS entities:
 - sub-<label> : subject identifier
 - ses-<label> : session identifier
 - task-<label> : task name (baseline, walking1, walking2)
 - run-<index> : run number
Citation
If you use this dataset, please cite the associated study and acknowledge the contributors.
Cohort#
Dataset Statistics#
Channel counts: 64 ch (n=14 recordings)
Sampling frequencies: 5000.0 Hz (n=14 recordings)
Total recording duration: 2 h 6 min
Signal · Electrodes & live trace#
Live trace viewer — sub-01 · ses-01 · task-walking2
Showing one representative recording out of
5 subjects and 14 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 · 64 sensors — 64 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 |
Dataset: T1-Weighted Structural MRI and fMRI of Participants Viewing Self-Avatar Exoskeleton Walking (11 SWS Cycles) |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Shantanu Sarkar, Kevin Nathan, Jose L. Contreras-Vidal |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds006945,
title = {Dataset: T1-Weighted Structural MRI and fMRI of Participants Viewing Self-Avatar Exoskeleton Walking (11 SWS Cycles)},
author = {Shantanu Sarkar and Kevin Nathan and Jose L. Contreras-Vidal},
doi = {10.18112/openneuro.ds006945.v1.2.1},
url = {https://doi.org/10.18112/openneuro.ds006945.v1.2.1},
}
API Reference#
eegdash.datasetEEGDashDatasetDS006945 · Sarkar2025_T1_Weighted_Structuraleegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS006945(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Dataset: T1-Weighted Structural MRI and fMRI of Participants Viewing Self-Avatar Exoskeleton Walking (11 SWS Cycles)
- Study:
ds006945(OpenNeuro)- Author (year):
Sarkar2025_T1_Weighted_Structural- Canonical:
—
Also importable as:
DS006945,Sarkar2025_T1_Weighted_Structural.Modality:
eeg; Experiment type:Motor; Subject type:Healthy. Subjects: 5; recordings: 14; tasks: 3.- 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/ds006945 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006945 DOI: https://doi.org/10.18112/openneuro.ds006945.v1.2.1
Examples
>>> from eegdash.dataset import DS006945 >>> dataset = DS006945(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/ds006945").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 ds006945 to reproduce the tutorial on this dataset.
Citation
Shantanu Sarkar, Kevin Nathan, Jose L. Contreras-Vidal (n.d.). Dataset: T1-Weighted Structural MRI and fMRI of Participants Viewing Self-Avatar Exoskeleton Walking (11 SWS Cycles). 10.18112/openneuro.ds006945.v1.2.1
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds006945.v1.2.1.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset