DS006839#

EEG recordings during sham neurofeedback in virtual reality

Access recordings and metadata through EEGDash.

Citation: C. Brigitte Aguilar Gonzales, Collaborators from the Experimental and Computational Neuroscience Group (2025). EEG recordings during sham neurofeedback in virtual reality. 10.18112/openneuro.ds006839.v1.0.0

Modality: eeg Subjects: 36 Recordings: 1125 License: CC0 Source: openneuro

Metadata: Complete (100%)

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.

View full README

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.

Dataset Information#

Dataset ID

DS006839

Title

EEG recordings during sham neurofeedback in virtual reality

Year

2025

Authors

  1. Brigitte Aguilar Gonzales, Collaborators from the Experimental and Computational Neuroscience Group

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds006839.v1.0.0

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},
}

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

  • Subjects: 36

  • Recordings: 1125

  • Tasks: 4

Channels & sampling rate

  • Channels: 29

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 0.0

Tags

  • Pathology: Healthy

  • Modality: Multisensory

  • Type: Attention

Files & format

  • Size on disk: 10.4 GB

  • File count: 1125

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds006839.v1.0.0

Provenance

API Reference#

Use the DS006839 class to access this dataset programmatically.

class eegdash.dataset.DS006839(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006839. 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

query#

Merged query with the dataset filter applied.

Type:

dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and 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

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#