EEGdash›OpenNeuro›DS005935
Iss. 5935 · 21 subjects · 64 recordings · CC0
Dataset Brief · Mirror Neuron Study

DS005935: fnirs dataset, 21 subjects#

Mirror Neuron Study

Citation: Xinge Li, Manon A. Krol, Sahar Jahani, David A. Boas, Helen Tager-Flusberg, Meryem A. Yücel (—). Mirror Neuron Study. 10.18112/openneuro.ds005935.v1.0.0

21-participant fNIRS dataset — Mirror Neuron Study.

Data & curation Xinge Li · Manon A. Krol · Sahar Jahani · David A. Boas · Helen Tager-Flusberg · Meryem A. Yücel
Funding National Institutes of Health (P41EB015896, P01-HD064653, NICHD-PO1 HD 64653)
fNIRS · 120 ch25 HzBIDS 1.8.0Task · MirrorNeuronVisualMotor
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#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005935

dataset = DS005935(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS005935(cache_dir="./data", subject="01")

Advanced query

dataset = DS005935(
    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{ds005935,
  title = {Mirror Neuron Study},
  author = {Xinge Li and Manon A. Krol and Sahar Jahani and David A. Boas and Helen Tager-Flusberg and Meryem A. Yücel},
  doi = {10.18112/openneuro.ds005935.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005935.v1.0.0},
}
§ 02Study · The README

About This Dataset#

No README content is available for this dataset.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 120 ch (n=64 recordings)

Sampling frequencies (Hz)

2525.0

Total recording duration: 6 h 8 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 120 ch · fNIRS · 25 Hz · 21 subjects, 64 recordings
Electrode layout — fNIRS · 48 sensors — 48 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.

NEMAR pipeline statistics — DS005935
HED event descriptors word cloud HED event descriptors word cloud — DS005935
§ 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…
Full dataset metadata table

Dataset ID

DS005935

Title

Mirror Neuron Study

Author (year)

Li2025

Canonical

—

Importable as

DS005935, Li2025

Year

—

Authors

Xinge Li, Manon A. Krol, Sahar Jahani, David A. Boas, Helen Tager-Flusberg, Meryem A. Yücel

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds005935.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds005935,
  title = {Mirror Neuron Study},
  author = {Xinge Li and Manon A. Krol and Sahar Jahani and David A. Boas and Helen Tager-Flusberg and Meryem A. Yücel},
  doi = {10.18112/openneuro.ds005935.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005935.v1.0.0},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS005935(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Li2025
Canonical—
Importable asDS005935 · Li2025
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS005935(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Mirror Neuron Study

Study:

ds005935 (OpenNeuro)

Author (year):

Li2025

Canonical:

—

Also importable as: DS005935, Li2025.

Modality: fnirs; Experiment type: Motor; Subject type: Unknown. Subjects: 21; recordings: 64; tasks: 1.

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/ds005935 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005935 DOI: https://doi.org/10.18112/openneuro.ds005935.v1.0.0

Examples

>>> from eegdash.dataset import DS005935
>>> dataset = DS005935(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/ds005935 · pull with datasets.load_dataset("EEGDash/ds005935").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS005935.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 ds005935 to reproduce the tutorial on this dataset.

Citation

Xinge Li, Manon A. Krol, Sahar Jahani, David A. Boas, Helen Tager-Flusberg, … (n.d.). Mirror Neuron Study. 10.18112/openneuro.ds005935.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.ds005935.v1.0.0.

BIDS
BIDS 1.8.0
Sidecars
not yet probed
Machine-readable
schema.org/Dataset · Croissant

See Also#