EEGdash›OpenNeuro›DS004830
Iss. 4830 · 12 subjects · 14 recordings · CC0
Dataset Brief · Spatial Attention Decoding using fNIRS During Complex Scene A…

DS004830: fnirs dataset, 12 subjects#

Spatial Attention Decoding using fNIRS During Complex Scene Analysis

Citation: Matthew Ning, Sudan Duwadi, Meryem A. Yucel, Alexander Von Luhmann, David A. Boas, Kamal Sen (20). Spatial Attention Decoding using fNIRS During Complex Scene Analysis. 10.18112/openneuro.ds004830.v2.0.0

12-participant fNIRS dataset — Spatial Attention Decoding using fNIRS During Complex Scene Analysis.

Data & curation Matthew Ning · Sudan Duwadi · Meryem A. Yucel · Alexander Von Luhmann · David A. Boas · Kamal Sen
Year 20 · Distributed via OpenNeuro
fNIRS · 72 (27), 84 (6) ch50 HzBIDS 1.7.1Task · overtHealthyVisualAttention
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 DS004830

dataset = DS004830(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004830(cache_dir="./data", subject="01")

Advanced query

dataset = DS004830(
    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{ds004830,
  title = {Spatial Attention Decoding using fNIRS During Complex Scene Analysis},
  author = {Matthew Ning and Sudan Duwadi and Meryem A. Yucel and Alexander Von Luhmann and David A. Boas and Kamal Sen},
  doi = {10.18112/openneuro.ds004830.v2.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004830.v2.0.0},
}
§ 02Study · The README

About This Dataset#

This dataset comes with published paper which can be found in https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2024.1329086/full

Please cite the paper if you use this dataset for your publication.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts (ch)

7284

Sampling frequencies (Hz)

5050.0

Total recording duration: 10 h 1 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 72 (27), 84 (6) ch · fNIRS · 50 Hz · 12 subjects, 14 recordings
Electrode layout — fNIRS · 35 sensors — 35 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 — DS004830
HED event descriptors word cloud HED event descriptors word cloud — DS004830
§ 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

DS004830

Title

Spatial Attention Decoding using fNIRS During Complex Scene Analysis

Author (year)

Ning2023

Canonical

—

Importable as

DS004830, Ning2023

Year

20

Authors

Matthew Ning, Sudan Duwadi, Meryem A. Yucel, Alexander Von Luhmann, David A. Boas, Kamal Sen

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004830.v2.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004830,
  title = {Spatial Attention Decoding using fNIRS During Complex Scene Analysis},
  author = {Matthew Ning and Sudan Duwadi and Meryem A. Yucel and Alexander Von Luhmann and David A. Boas and Kamal Sen},
  doi = {10.18112/openneuro.ds004830.v2.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004830.v2.0.0},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS004830(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Ning2023
Canonical—
Importable asDS004830 · Ning2023
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS004830(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Spatial Attention Decoding using fNIRS During Complex Scene Analysis

Study:

ds004830 (OpenNeuro)

Author (year):

Ning2023

Canonical:

—

Also importable as: DS004830, Ning2023.

Modality: fnirs; Experiment type: Attention; Subject type: Healthy. Subjects: 12; recordings: 14; tasks: 1.

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/ds004830 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004830 DOI: https://doi.org/10.18112/openneuro.ds004830.v2.0.0

Examples

>>> from eegdash.dataset import DS004830
>>> dataset = DS004830(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 FacePre-bundled mirror at EEGDash/ds004830 · pull with datasets.load_dataset("EEGDash/ds004830").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS004830.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 ds004830 to reproduce the tutorial on this dataset.

Citation

Matthew Ning, Sudan Duwadi, Meryem A. Yucel, Alexander Von Luhmann, David A. Boas, … (20). Spatial Attention Decoding using fNIRS During Complex Scene Analysis. 10.18112/openneuro.ds004830.v2.0.0

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds004830.v2.0.0.

BIDS
BIDS 1.7.1
Sidecars
not yet probed
Machine-readable
schema.org/Dataset · Croissant

See Also#