DS007473: fnirs dataset, 5 subjects#
High-Density Diffuse Optical Tomography Audiovisual Movie Viewing Dataset
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 (20). High-Density Diffuse Optical Tomography Audiovisual Movie Viewing Dataset. 10.18112/openneuro.ds007473.v1.0.0
5-participant fNIRS dataset — High-Density Diffuse Optical Tomography Audiovisual Movie Viewing Dataset.
Quickstart#
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)
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'])
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
Cohort#
Dataset Statistics#
Channel counts (ch)
Sampling frequencies: 10.41666666666667 Hz (n=189 recordings)
Total recording duration: 51 h
Signal · Electrodes & live trace#
Electrode layout — fNIRS · 253 sensors — 253 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
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.
Full dataset metadata table
Dataset ID |
|
Title |
High-Density Diffuse Optical Tomography Audiovisual Movie Viewing Dataset |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
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 |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS007473 · Fogarty2026_Higheegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS007473(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand 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)
- 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.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorch
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 ds007473 to reproduce the tutorial on this dataset.
Citation
Morgan Fogarty, Kalyan Tripathy, Alexandra M Svoboda, Mariel L Schroeder, Sean M Rafferty, … (20). High-Density Diffuse Optical Tomography Audiovisual Movie Viewing Dataset. 10.18112/openneuro.ds007473.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.ds007473.v1.0.0.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset