DS006839: eeg dataset, 36 subjects#
EEG recordings during sham neurofeedback in virtual reality
Citation: C. Brigitte Aguilar Gonzales, Collaborators from the Experimental and Computational Neuroscience Group (2019). EEG recordings during sham neurofeedback in virtual reality. 10.18112/openneuro.ds006839.v1.0.0
36-participant EEG dataset — EEG recordings during sham neurofeedback in virtual reality.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS006839
dataset = DS006839(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS006839(cache_dir="./data", subject="01")
Advanced query
dataset = DS006839(
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{ds006839,
title = {EEG recordings during sham neurofeedback in virtual reality},
author = {C. Brigitte Aguilar Gonzales and Collaborators from the Experimental and Computational Neuroscience Group},
doi = {10.18112/openneuro.ds006839.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds006839.v1.0.0},
}
About This Dataset#
EEG recordings during sham neurofeedback in virtual reality
Description This dataset contains EEG recordings acquired during a sham neurofeedback experiment conducted in a virtual reality (VR) environment. The study aimed to investigate how feedback valence (positive, negative, or control) modulates alpha-band activity and during an attentional task. EEG signals were recorded using a 32-channel SynAmps RT amplifier (Compumedics NeuroScan Inc., Charlotte, NC, USA) and Ag/AgCl passive electrodes mounted on an elastic cap (Wuhan Greentek Pty. Ltd., China) following the extended 10–20 international system.
Each participant completed four conditions:
Positive feedback (S##_p.cnt) - sham feedback with a reinforcement valence.
Negative feedback (S##_n.cnt) - sham feedback with a punishment valence. Control (S##_c.cnt) — participants observed the VR environment without any feedback.
Resting-state (S##_resting.cnt) — participants alternated between eyes open and eyes closed conditions. Experimental design Feedback blocks: Each feedback condition consisted of four blocks of approximately 2 minutes each.
Events: 238 — marks the beginning of each 2-minute feedback block. 222 — indicates an increase in brightness or volume of VR objects. 190 — indicates a decrease in brightness or volume. 126 — marks the beginning and end of eyes open/closed periods during the resting condition.
Resting-state order: Eyes open first, followed by eyes closed. Data format Original EEG recordings were collected in .cnt format (NeuroScan).
Data were converted to the Brain Imaging Data Structure (BIDS) format using the MNE-BIDS toolbox (Appelhoff et al., 2019). Each subject folder (e.g., sub-01/) contains EEG data files (.eeg), event markers, and corresponding JSON sidecar files with acquisition parameters.
Data availability The BIDS-formatted dataset is publicly available on the OpenNeuro repository and linked through the OSF Wiki project.
References Appelhoff, S., Sanderson, M., Brooks, T. L., van Vliet, M., Quentin, R., Holdgraf, C., … Gramfort, A. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software, 4(44), 1896. https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., & Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103.
Cohort#
Dataset Statistics#
Channel counts: 29 ch (n=144 recordings)
Sampling frequencies: 1000.0 Hz (n=144 recordings)
Total recording duration: 26 h
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-positive
Showing one representative recording out of
36 subjects and 144 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 · 29 sensors — 29 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 |
EEG recordings during sham neurofeedback in virtual reality |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2019 |
Authors |
|
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds006839,
title = {EEG recordings during sham neurofeedback in virtual reality},
author = {C. Brigitte Aguilar Gonzales and Collaborators from the Experimental and Computational Neuroscience Group},
doi = {10.18112/openneuro.ds006839.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds006839.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS006839 · Gonzales2025eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS006839(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
EEG recordings during sham neurofeedback in virtual reality
- Study:
ds006839(OpenNeuro)- Author (year):
Gonzales2025- Canonical:
—
Also importable as:
DS006839,Gonzales2025.Modality:
eeg; Experiment type:Attention; Subject type:Healthy. Subjects: 36; recordings: 144; tasks: 4.- 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/ds006839 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006839 DOI: https://doi.org/10.18112/openneuro.ds006839.v1.0.0
Examples
>>> from eegdash.dataset import DS006839 >>> dataset = DS006839(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/ds006839").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 ds006839 to reproduce the tutorial on this dataset.
Citation
C. Brigitte Aguilar Gonzales, Collaborators from the Experimental and Computational Neuroscience Group (2019). EEG recordings during sham neurofeedback in virtual reality. 10.18112/openneuro.ds006839.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.ds006839.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset