EEGdash›OpenNeuro›DS006921

Iss. 6921 · 38 subjects · 152 recordings · CC0

Dataset Brief · High Density Resting State EEG of Phantom Limb Pain and Controls

DS006921: eeg dataset, 38 subjects#

High Density Resting State EEG of Phantom Limb Pain and Controls

Citation: Ramne, M., Damercheli, S., Apelgren, F., Pettersson, I., Lendaro, E. (2024). High Density Resting State EEG of Phantom Limb Pain and Controls. 10.18112/openneuro.ds006921.v1.1.1

38-participant EEG dataset — High Density Resting State EEG of Phantom Limb Pain and Controls.

Data & curation Ramne, M. · Damercheli, S. · Apelgren, F. · Pettersson, I. · Lendaro, E.
Year 2024 · Distributed via OpenNeuro

EEG · 128 (124), 64 (28) ch2400 HzBIDS 1.9.02 tasks5 sessionsOtherResting StateClinical/Intervention

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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},
}

§ 02Study · The README

About This Dataset#

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.

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:

High Density Resting State EEG of Phantom Limb Pain and Controls

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

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution (n=38, range 22–77 yr, mean 36.8 yr · sex per subject not reported)

20253540455055606575

Sex composition

Female
18
Male
20
F : M ratio
0.90 : 1

47% female · n = 38 subjects with reported sex.

Channel counts (ch)

64128

Sampling frequencies: 2400.0 Hz (n=152 recordings)

Total recording duration: 16 h 57 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 128 (124), 64 (28) ch · EEG · 2400 Hz · 38 subjects, 152 recordings

Live trace viewer — sub-14 · ses-2 · task-EYESCLOSED · run-4

Showing one representative recording out of 38 subjects and 152 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 · 128 sensors — 128 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

§ 05Manifest · BIDS tree

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.DS006921(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Ramne2025

Canonical—

Importable asDS006921 · Ramne2025

Sourceeegdash/dataset/registry.py · [source ↗]

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

High Density Resting State EEG of Phantom Limb Pain and Controls

Study:: ds006921 (OpenNeuro)
Author (year):: Ramne2025
Canonical:: —

Also importable as: DS006921, Ramne2025.

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 DOI: https://doi.org/10.18112/openneuro.ds006921.v1.1.1

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: 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.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FacePre-bundled mirror at EEGDash/ds006921 · pull with datasets.load_dataset("EEGDash/ds006921").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS006921.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

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 ds006921 to reproduce the tutorial on this dataset.

Citation

Ramne, M., Damercheli, S., Apelgren, F., Pettersson, I., Lendaro, E. (2024). High Density Resting State EEG of Phantom Limb Pain and Controls. 10.18112/openneuro.ds006921.v1.1.1

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds006921.v1.1.1.

Related & sibling datasets

DS006923EEG · 140 subj DS007427EEG · 44 subj DS007347EEG · 5 subj DS007020EEG · 94 subj DS007558EEG · 67 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

channels · electrodes · coordsystem · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds006921.v1.1.1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS006921`
Title	High Density Resting State EEG of Phantom Limb Pain and Controls
Author (year)	`Ramne2025`
Canonical	—
Importable as	`DS006921`, `Ramne2025`
Year	2024
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

DS006921: eeg dataset, 38 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#