DS007006: eeg dataset, 10 subjects#
VR-Compassion Cultivation Training
Citation: Ying Wu, Enrique Carrillosulub, Leon Lange, Chloe Tanega, Nicole Wells, Erik Virre, Cassandra Vieten (—). VR-Compassion Cultivation Training. 10.18112/openneuro.ds007006.v1.0.0
10-participant EEG dataset — VR-Compassion Cultivation Training.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS007006
dataset = DS007006(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS007006(cache_dir="./data", subject="01")
Advanced query
dataset = DS007006(
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{ds007006,
title = {VR-Compassion Cultivation Training},
author = {Ying Wu and Enrique Carrillosulub and Leon Lange and Chloe Tanega and Nicole Wells and Erik Virre and Cassandra Vieten},
doi = {10.18112/openneuro.ds007006.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds007006.v1.0.0},
}
About This Dataset#
VR-CCT Dataset
Compassion Island was a social world implemented in AltspaceVR by tecchnology collaborators Origami Air.
It was specifically created for the study of VR-based
augmentation of compassion cultivation training (CCT).
It featured three main settings – a meditation hall, a garden courtyard with a large willow tree, and a clinic. During experimental sessions, participants interacted with two characters in these spaces, represented as avatars – namely, a guide, who helped the volunteer navigate from setting to setting and offered other assistance as needed, and Ivan, who was an agitated patient in the clinic. Both characters were animated by live actors in separate locations.
Participants were able to converse freely with these characters whenever they were co-present with either character in the same space. All sessions began in the meditation hall, which featured a pulsating orb designed to help participants regulate their breathing during an audio-recorded guided meditation. Next, participants were ushered outside to the garden, where they were invited to contemplate a tree with a glowing core while listening to an audio-recorded compassion meditation and performing visualization exercises that centered on universal compassion for all beings.
Lastly, participants were directed into a virtual clinic to converse with Ivan, an agitated patient waiting inside the clinic, where participants would have the opportunity to practice exercising the feeling of universal compassion from the garden meditation.
Cohort#
Dataset Statistics#
Age distribution by gender (n=10, range 23–59 yr, mean 38.6 yr)
Sex composition
Channel counts: 64 ch (n=50 recordings)
Sampling frequencies: 256.0 Hz (n=50 recordings)
Total recording duration: 3 h 36 min
Signal · Electrodes & live trace#
Live trace viewer — sub-010 · task-compTree
Showing one representative recording out of
10 subjects and 50 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 |
VR-Compassion Cultivation Training |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Ying Wu, Enrique Carrillosulub, Leon Lange, Chloe Tanega, Nicole Wells, Erik Virre, Cassandra Vieten |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds007006,
title = {VR-Compassion Cultivation Training},
author = {Ying Wu and Enrique Carrillosulub and Leon Lange and Chloe Tanega and Nicole Wells and Erik Virre and Cassandra Vieten},
doi = {10.18112/openneuro.ds007006.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds007006.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS007006 · Wu2025eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS007006(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
VR-Compassion Cultivation Training
- Study:
ds007006(OpenNeuro)- Author (year):
Wu2025- Canonical:
—
Also importable as:
DS007006,Wu2025.Modality:
eeg; Experiment type:Affect; Subject type:Healthy. Subjects: 10; recordings: 50; tasks: 5.- 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/ds007006 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007006 DOI: https://doi.org/10.18112/openneuro.ds007006.v1.0.0
Examples
>>> from eegdash.dataset import DS007006 >>> dataset = DS007006(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/ds007006").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 ds007006 to reproduce the tutorial on this dataset.
Citation
Ying Wu, Enrique Carrillosulub, Leon Lange, Chloe Tanega, Nicole Wells, … (n.d.). VR-Compassion Cultivation Training. 10.18112/openneuro.ds007006.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.ds007006.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset