DS007473: fnirs dataset, 5 subjects#

High-Density Diffuse Optical Tomography Audiovisual Movie Viewing Dataset

Access recordings and metadata through EEGDash.

Citation: Morgan Fogarty, Kalyan Tripathy, Alexandra M Svoboda, Mariel L Schroeder, Sean M Rafferty, Edward J Richter, Christopher Tracy, Patricia K Mansfield, Madison Booth, Andrew K Fishell, Arefeh Sherafati, Zachary E Markow, Muriah D Wheelock, Ana Maria Arbelaez, Bradley L Schlaggar, Christopher D Smyser, Adam T Eggebrecht, Joseph P Culver (2026). High-Density Diffuse Optical Tomography Audiovisual Movie Viewing Dataset. 10.18112/openneuro.ds007473.v1.0.0

Modality: fnirs Subjects: 5 Recordings: 189 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007473

dataset = DS007473(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 = DS007473(cache_dir="./data", subject="01")

Advanced query

dataset = DS007473(
    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{ds007473,
  title = {High-Density Diffuse Optical Tomography Audiovisual Movie Viewing Dataset},
  author = {Morgan Fogarty and Kalyan Tripathy and Alexandra M Svoboda and Mariel L Schroeder and Sean M Rafferty and Edward J Richter and Christopher Tracy and Patricia K Mansfield and Madison Booth and Andrew K Fishell and Arefeh Sherafati and Zachary E Markow and Muriah D Wheelock and Ana Maria Arbelaez and Bradley L Schlaggar and Christopher D Smyser and Adam T Eggebrecht and Joseph P Culver},
  doi = {10.18112/openneuro.ds007473.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007473.v1.0.0},
}

About This Dataset#

This dataset consists of 5 participants completing functional localizer and movie viewing tasks. These data are stored in SNIRF format with optode and landmark locations associated with subject specific head models. See the corresponding publication for more information about this dataset: Tripathy K, Fogarty M, et al., “Mapping brain function in adults and young children during naturalistic viewing with high-density diffuse optical tomography.” Human Brain Mapping. 2024 May;45(7):e26684. doi: 10.1002/hbm.26684. PMID: 38703090; PMCID: PMC11069306. Kalyan Tripathy, Zachary E. Markow, Morgan Fogarty, Mariel L. Schroeder, Alexa M. Svoboda, Adam T. Eggebrecht, Bradley L. Schlaggar, Jason W. Trobaugh, Joseph P. Culver, “Multisensory naturalistic decoding with high-density diffuse optical tomography,” Neurophoton. 12(1) 015002 (23 January 2025) https://doi.org/10.1117/1.NPh.12.1.015002

Dataset Information#

Dataset ID	`DS007473`
Title	High-Density Diffuse Optical Tomography Audiovisual Movie Viewing Dataset
Author (year)	`Fogarty2026_High`
Canonical	—
Importable as	`DS007473`, `Fogarty2026_High`
Year	2026
Authors	Morgan Fogarty, Kalyan Tripathy, Alexandra M Svoboda, Mariel L Schroeder, Sean M Rafferty, Edward J Richter, Christopher Tracy, Patricia K Mansfield, Madison Booth, Andrew K Fishell, Arefeh Sherafati, Zachary E Markow, Muriah D Wheelock, Ana Maria Arbelaez, Bradley L Schlaggar, Christopher D Smyser, Adam T Eggebrecht, Joseph P Culver
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007473.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

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: 5
Recordings: 189
Tasks: 19

Channels & sampling rate

Channels: 6782 (71), 6928 (43), 6880 (38), 6750 (31), 7030 (6)
Sampling rate (Hz): 10.41666666666667
Duration (hours): 51.12245333333332

Electrode Layout#

Electrode layout — fNIRS · 253 sensors — 253 channels

Dataset Statistics#

Channel counts (ch)

67506782688069287030

Sampling frequencies: 10.41666666666667 Hz (n=189 recordings)

Total recording duration: 51 h

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the DS007473 class to access this dataset programmatically.

class eegdash.dataset.DS007473(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

High-Density Diffuse Optical Tomography Audiovisual Movie Viewing Dataset

Study:: ds007473 (OpenNeuro)
Author (year):: Fogarty2026_High
Canonical:: —

Also importable as: DS007473, Fogarty2026_High.

Modality: fnirs; Experiment type: Perception; Subject type: Healthy. Subjects: 5; recordings: 189; tasks: 19.

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/ds007473 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007473 DOI: https://doi.org/10.18112/openneuro.ds007473.v1.0.0

Examples

>>> from eegdash.dataset import DS007473
>>> dataset = DS007473(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)