EEGdash›OpenNeuro›DS007420

Iss. 7420 · 12 subjects · 60 recordings · CC0

Dataset Brief · A Light Weight Multi-Distance fNIRS Dataset for Ball-Squeezin…

DS007420: fnirs dataset, 12 subjects#

A Light Weight Multi-Distance fNIRS Dataset for Ball-Squeezing Task and Purposeful Motion Artifact Creation Task

Citation: Gao, Yuanyuan, Rogers, De’Ja, von Lühmann, Alexander, Ortega-Martinez, Antonio, Boas, David, Yücel, Meryem (—). A Light Weight Multi-Distance fNIRS Dataset for Ball-Squeezing Task and Purposeful Motion Artifact Creation Task. 10.18112/openneuro.ds007420.v1.0.2

12-participant fNIRS dataset — A Light Weight Multi-Distance fNIRS Dataset for Ball-Squeezing Task and Purposeful Motion Artifact Creation Task.

Data & curation Gao, Yuanyuan · Rogers, De’Ja · von Lühmann, Alexander · Ortega-Martinez, Antonio · Boas, David · Yücel, Meryem
Funding NIH BRAIN Initiative (Grant No. 1U01EB029856-01)

fNIRS · 200 ch9 Hz · mixedBIDS 1.7.14 tasks3 sessionsHealthyMotorMotor

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 DS007420

dataset = DS007420(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 = DS007420(cache_dir="./data", subject="01")

Advanced query

dataset = DS007420(
    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{ds007420,
  title = {A Light Weight Multi-Distance fNIRS Dataset for Ball-Squeezing Task and Purposeful Motion Artifact Creation Task},
  author = {Gao, Yuanyuan and Rogers, De’Ja and von Lühmann, Alexander and Ortega-Martinez, Antonio and Boas, David and Yücel, Meryem},
  doi = {10.18112/openneuro.ds007420.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds007420.v1.0.2},
}

§ 02Study · The README

About This Dataset#

No README content is available for this dataset.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 200 ch (n=60 recordings)

Sampling frequencies (Hz)

8.78.711.611.6

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 200 ch · fNIRS · 9 Hz · mixed · 12 subjects, 60 recordings

Electrode layout — fNIRS · 46 sensors — 46 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.DS007420(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Gao2026_Light_Weight_Multi

Canonical—

Importable asDS007420 · Gao2026_Light_Weight_Multi

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

class eegdash.dataset.DS007420(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

A Light Weight Multi-Distance fNIRS Dataset for Ball-Squeezing Task and Purposeful Motion Artifact Creation Task

Study:: ds007420 (OpenNeuro)
Author (year):: Gao2026_Light_Weight_Multi
Canonical:: —

Also importable as: DS007420, Gao2026_Light_Weight_Multi.

Modality: fnirs; Experiment type: Motor; Subject type: Healthy. Subjects: 12; recordings: 60; 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/ds007420 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007420 DOI: https://doi.org/10.18112/openneuro.ds007420.v1.0.2

Examples

>>> from eegdash.dataset import DS007420
>>> dataset = DS007420(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 FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS007420.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 ds007420 to reproduce the tutorial on this dataset.

Citation

Gao, Yuanyuan, Rogers, De’Ja, von Lühmann, Alexander, Ortega-Martinez, Antonio, Boas, David, … (n.d.). A Light Weight Multi-Distance fNIRS Dataset for Ball-Squeezing Task and Purposeful Motion Artifact Creation Task. 10.18112/openneuro.ds007420.v1.0.2

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds007420.v1.0.2.

Related & sibling datasets

DS005929FNIRS · 7 subj DS006903FNIRS · 17 subj DS006673FNIRS · 17 subj DS004929FNIRS · 12 subjSame authors DS005930FNIRS · 12 subjSame authors

+ 1 more — see See Also below →

BIDS

BIDS 1.7.1

Sidecars

events · events.json · channels · coordsystem

Provenance

CC0 · 10.18112/openneuro.ds007420.v1.0.2

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`DS007420`
Title	A Light Weight Multi-Distance fNIRS Dataset for Ball-Squeezing Task and Purposeful Motion Artifact Creation Task
Author (year)	`Gao2026_Light_Weight_Multi`
Canonical	—
Importable as	`DS007420`, `Gao2026_Light_Weight_Multi`
Year	—
Authors	Gao, Yuanyuan, Rogers, De’Ja, von Lühmann, Alexander, Ortega-Martinez, Antonio, Boas, David, Yücel, Meryem
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007420.v1.0.2
Source links	OpenNeuro \| NeMAR \| Source URL

DS007420: fnirs dataset, 12 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#