EEGdash›OpenNeuro›DS007463
Iss. 7463 · 8 subjects · 88 recordings · CC0
Dataset Brief · Very-High-Density Diffuse Optical Tomography System Validatio…

DS007463: fnirs dataset, 8 subjects#

Very-High-Density Diffuse Optical Tomography System Validation Dataset

Citation: Morgan Fogarty, Sean M. Rafferty, Zachary E. Markow, Anthony C. O’Sullivan, Calamity F. Svoboda, Tessa George, Kelsey King, Dana Wilhelm, Kalyan Tripathy, Emily M. Mugler, Stephanie Naufel, Allen Yin, Jason W. Trobaugh, Adam T. Eggebrecht, Edward J. Richter, Joseph P. Culver (20). Very-High-Density Diffuse Optical Tomography System Validation Dataset. 10.18112/openneuro.ds007463.v1.1.1

8-participant fNIRS dataset — Very-High-Density Diffuse Optical Tomography System Validation Dataset.

Data & curation Morgan Fogarty · Sean M. Rafferty · Zachary E. Markow · Anthony C. O’Sullivan · Calamity F. Svoboda · Tessa George · …
Year 20 · Distributed via OpenNeuro
fNIRS · 19086 (14), 19620 (11), 21518 (11), 19426 (11), 19528 (11), 19908 (10), 20218 (10), 20874 (10) ch8 Hz14 tasks2 sessionsHealthyVisualPerception
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 DS007463

dataset = DS007463(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS007463(cache_dir="./data", subject="01")

Advanced query

dataset = DS007463(
    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{ds007463,
  title = {Very-High-Density Diffuse Optical Tomography System Validation Dataset},
  author = {Morgan Fogarty and Sean M. Rafferty and Zachary E. Markow and Anthony C. O’Sullivan and Calamity F. Svoboda and Tessa George and Kelsey King and Dana Wilhelm and Kalyan Tripathy and Emily M. Mugler and Stephanie Naufel and Allen Yin and Jason W. Trobaugh and Adam T. Eggebrecht and Edward J. Richter and Joseph P. Culver},
  doi = {10.18112/openneuro.ds007463.v1.1.1},
  url = {https://doi.org/10.18112/openneuro.ds007463.v1.1.1},
}
§ 02Study · The README

About This Dataset#

This dataset consists of 8 participants completing functional localizer and movie-viewing tasks in both Very High Density Diffuse Optical Tomography (VHD-DOT) and fMRI. Sessions 1 and 2 for each subject include the VHD-DOT data in SNIRF format while sessions 3 or more include the fMRI data in NIFTI format.

Preprocessed fMRI data used for comparisons to VHD-DOT are in the /derivatives folder and are in NIFTI format.

More information on this data can be found here:

Morgan Fogarty, Sean M. Rafferty, Zachary E. Markow, Anthony C. O’Sullivan, Calamity F. Svoboda, Tessa George, Kelsey King, Dana Wilhelm, Kalyan Tripathy, Emily M. Mugler, Stephanie Naufel, Allen Yin, Jason W. Trobaugh, Adam T. Eggebrecht, Edward J. Richter, Joseph P. Culver; Functional brain mapping using whole-head very high-density diffuse optical tomography. Imaging Neuroscience 2025; 3 IMAG.a.54. doi: https://doi.org/10.1162/IMAG.a.54

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts (ch)

1908619426195281962019908202182087421518

Sampling frequencies: 7.8125 Hz (n=88 recordings)

Total recording duration: 18 h 53 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 19086 (14), 19620 (11), 21518 (11), 19426 (11), 19528 (11), 19908 (10), 20218 (10), 20874 (10) ch · fNIRS · 8 Hz · 8 subjects, 88 recordings
Electrode layout — fNIRS · 507 sensors — 507 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 — DS007463
HED event descriptors word cloud HED event descriptors word cloud — DS007463
§ 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

DS007463

Title

Very-High-Density Diffuse Optical Tomography System Validation Dataset

Author (year)

Fogarty2026_Very

Canonical

—

Importable as

DS007463, Fogarty2026_Very

Year

20

Authors

Morgan Fogarty, Sean M. Rafferty, Zachary E. Markow, Anthony C. O’Sullivan, Calamity F. Svoboda, Tessa George, Kelsey King, Dana Wilhelm, Kalyan Tripathy, Emily M. Mugler, Stephanie Naufel, Allen Yin, Jason W. Trobaugh, Adam T. Eggebrecht, Edward J. Richter, Joseph P. Culver

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds007463.v1.1.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds007463,
  title = {Very-High-Density Diffuse Optical Tomography System Validation Dataset},
  author = {Morgan Fogarty and Sean M. Rafferty and Zachary E. Markow and Anthony C. O’Sullivan and Calamity F. Svoboda and Tessa George and Kelsey King and Dana Wilhelm and Kalyan Tripathy and Emily M. Mugler and Stephanie Naufel and Allen Yin and Jason W. Trobaugh and Adam T. Eggebrecht and Edward J. Richter and Joseph P. Culver},
  doi = {10.18112/openneuro.ds007463.v1.1.1},
  url = {https://doi.org/10.18112/openneuro.ds007463.v1.1.1},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS007463(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Fogarty2026_Very
Canonical—
Importable asDS007463 · Fogarty2026_Very
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS007463(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Very-High-Density Diffuse Optical Tomography System Validation Dataset

Study:

ds007463 (OpenNeuro)

Author (year):

Fogarty2026_Very

Canonical:

—

Also importable as: DS007463, Fogarty2026_Very.

Modality: fnirs; Experiment type: Perception; Subject type: Healthy. Subjects: 8; recordings: 88; tasks: 14.

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/ds007463 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007463 DOI: https://doi.org/10.18112/openneuro.ds007463.v1.1.1

Examples

>>> from eegdash.dataset import DS007463
>>> dataset = DS007463(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 — DS007463.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 ds007463 to reproduce the tutorial on this dataset.

Citation

Morgan Fogarty, Sean M. Rafferty, Zachary E. Markow, Anthony C. O’Sullivan, Calamity F. Svoboda, … (20). Very-High-Density Diffuse Optical Tomography System Validation Dataset. 10.18112/openneuro.ds007463.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.ds007463.v1.1.1.

BIDS
version not on file
Sidecars
events · channels · coordsystem
Machine-readable
schema.org/Dataset · Croissant
Mirrors
OpenNeuro · NEMAR · HF org

See Also#