DS006921#

High Density Resting State EEG of Phantom Limb Pain and Controls

Access recordings and metadata through EEGDash.

Citation: Ramne, M., Damercheli, S., Apelgren, F., Pettersson, I., Lendaro, E. (2025). High Density Resting State EEG of Phantom Limb Pain and Controls. 10.18112/openneuro.ds006921.v1.1.1

Modality: eeg Subjects: 38 Recordings: 664 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006921

dataset = DS006921(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS006921(cache_dir="./data", subject="01")

Advanced query

dataset = DS006921(
    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{ds006921,
  title = {High Density Resting State EEG of Phantom Limb Pain and Controls},
  author = {Ramne, M. and Damercheli, S. and Apelgren, F. and Pettersson, I. and Lendaro, E.},
  doi = {10.18112/openneuro.ds006921.v1.1.1},
  url = {https://doi.org/10.18112/openneuro.ds006921.v1.1.1},
}

About This Dataset#

High Density Resting State EEG of Phantom Limb Pain and Controls

This dataset comprises resting state high density EEG data (64 or 128 channels) collected from three categories of subjects: amputees with phantom limb pain, amputees without phantom limb pain, and intact, pain free controls. The data has been organised according to the BIDS standard for more accessible reuse. Recordings are approximately 7 minutes long with eyes opened or closed, as indicated by task.

Usage

For loading and using the data in Matlab we recommend using pop_importbids by EEGLab, example usage here: https://eeglab.org/tutorials/11_Scripting/Analyzing_EEG_BIDS_data_in_EEGLAB.html

For a complete pipeline for resting state EEG preprocessing and feature extraction in Matlab we recommend DISCOVER-EEG: Cristina Gil. (2024). crisglav/discover-eeg: 2.0.0 (2.0.0). Zenodo. https://doi.org/10.5281/zenodo.10797803

Phenotype data note

Session-level questionnaire data are stored in phenotype/pain-questionnaire_sessions.tsv with descriptions of the corresponding questionnaire items in phenotype/pain-questionnaire_sessions.json. The phenotype files are currently ignored by the BIDS Validator due to incomplete support for phenotype indexing across multiple sessions.

License

CC0

Dataset Information#

Dataset ID

DS006921

Title

High Density Resting State EEG of Phantom Limb Pain and Controls

Year

2025

Authors

Ramne, M., Damercheli, S., Apelgren, F., Pettersson, I., Lendaro, E.

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds006921.v1.1.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds006921,
  title = {High Density Resting State EEG of Phantom Limb Pain and Controls},
  author = {Ramne, M. and Damercheli, S. and Apelgren, F. and Pettersson, I. and Lendaro, E.},
  doi = {10.18112/openneuro.ds006921.v1.1.1},
  url = {https://doi.org/10.18112/openneuro.ds006921.v1.1.1},
}

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: 38

  • Recordings: 664

  • Tasks: 2

Channels & sampling rate

  • Channels: 122 (124), 128 (124), 64 (28), 63 (28)

  • Sampling rate (Hz): 2400.0

  • Duration (hours): 0.0

Tags

  • Pathology: Other

  • Modality: Resting State

  • Type: Clinical/Intervention

Files & format

  • Size on disk: 64.4 GB

  • File count: 664

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds006921.v1.1.1

Provenance

API Reference#

Use the DS006921 class to access this dataset programmatically.

class eegdash.dataset.DS006921(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006921. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Other. Subjects: 38; recordings: 152; tasks: 2.

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/ds006921 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006921

Examples

>>> from eegdash.dataset import DS006921
>>> dataset = DS006921(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#