DS007738: fnirs dataset, 38 subjects#
Whole-Head Cocktail Party fNIRS
Citation: Sudan Duwadi, De’Ja Rogers, Alex D. Boyd, Laura B. Carlton, Yiwen Zhang, Anna Kawai Gaona, Aneesa Diya Pathiyaparambath, Ravin Chaudhury, Bernhard B Zimmermann, Walker J O’Brien, Alexander von Lühmann, David A. Boas, Meryem A. Yücel, Kamal Sen (—). Whole-Head Cocktail Party fNIRS. 10.18112/openneuro.ds007738.v1.0.0
38-participant fNIRS dataset — Whole-Head Cocktail Party fNIRS.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS007738
dataset = DS007738(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS007738(cache_dir="./data", subject="01")
Advanced query
dataset = DS007738(
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{ds007738,
title = {Whole-Head Cocktail Party fNIRS},
author = {Sudan Duwadi and De'Ja Rogers and Alex D. Boyd and Laura B. Carlton and Yiwen Zhang and Anna Kawai Gaona and Aneesa Diya Pathiyaparambath and Ravin Chaudhury and Bernhard B Zimmermann and Walker J O'Brien and Alexander von Lühmann and David A. Boas and Meryem A. Yücel and Kamal Sen},
doi = {10.18112/openneuro.ds007738.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds007738.v1.0.0},
}
About This Dataset#
Whole-Head Cocktail Party fNIRS
Whole-head functional near-infrared spectroscopy (fNIRS) recordings from 38 subjects performing covert and overt spatial-attention tasks in a cocktail-party paradigm, with simultaneous eye-tracking. fNIRS: 56 sources, 144 detectors, 1134 measurement channels at ~8.99 Hz.
- overt: Spatial attention with overt eye movements: subjects move
their eyes to attend to audiovisual stimuli (videos) on the cued side.
- covert: Covert auditory attention: audio-only stimuli; subjects
fixate the central plus sign and attend to left or right without moving their eyes.
- visualorient: Eye-movement-only baseline. Subjects make ~5 s eye orienting
and fixation movements with no audiovisual stimuli and no fixation cross. Matches the overt task structure.
- resting: Resting-state recording: 5 minutes of central-fixation on a
plus sign with no task.
- longvisualorient: Orienting + visual attention recording with events.tsv
aligned to the audiovisual orienting cue. Subjects orient their eyes to the cue; ~15-17 s later a video clip appears on the attended side. Same SNIRF recording as videoattend; only the events.tsv differs.
- videoattend: Same SNIRF recording as longvisualorient, with events.tsv
aligned to the onset of the video clip (~15-17 s after the orienting cue) for video-locked GLM analysis.
Privacy
This dataset contains only fNIRS optical measurements and eye-tracking time series; no anatomical scans (MRI, CT, photographs, or otherwise) are included. Facial defacing is therefore not applicable to this dataset.
Data were acquired and shared under approved IRB consent. Companion analysis code: duwadisudan/wholehead-cocktail-party-fnirs
Cohort#
Dataset Statistics#
Channel counts: 1134 ch (n=223 recordings)
Sampling frequencies (Hz)
Total recording duration: 36 h
Signal · Electrodes & live trace#
No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.
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 |
Whole-Head Cocktail Party fNIRS |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Sudan Duwadi, De’Ja Rogers, Alex D. Boyd, Laura B. Carlton, Yiwen Zhang, Anna Kawai Gaona, Aneesa Diya Pathiyaparambath, Ravin Chaudhury, Bernhard B Zimmermann, Walker J O’Brien, Alexander von Lühmann, David A. Boas, Meryem A. Yücel, Kamal Sen |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds007738,
title = {Whole-Head Cocktail Party fNIRS},
author = {Sudan Duwadi and De'Ja Rogers and Alex D. Boyd and Laura B. Carlton and Yiwen Zhang and Anna Kawai Gaona and Aneesa Diya Pathiyaparambath and Ravin Chaudhury and Bernhard B Zimmermann and Walker J O'Brien and Alexander von Lühmann and David A. Boas and Meryem A. Yücel and Kamal Sen},
doi = {10.18112/openneuro.ds007738.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds007738.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.DS007738(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Whole-Head Cocktail Party fNIRS
- Study:
ds007738(OpenNeuro)- Author (year):
nan- Canonical:
—
Also importable as:
DS007738,nan.Modality:
fnirs. Subjects: 38; recordings: 223; tasks: 6.- 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/ds007738 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007738 DOI: https://doi.org/10.18112/openneuro.ds007738.v1.0.0
Examples
>>> from eegdash.dataset import DS007738 >>> dataset = DS007738(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 ds007738 to reproduce the tutorial on this dataset.
Citation
Sudan Duwadi, De'Ja Rogers, Alex D. Boyd, Laura B. Carlton, Yiwen Zhang, … (n.d.). Whole-Head Cocktail Party fNIRS. 10.18112/openneuro.ds007738.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.ds007738.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset