NM000175: fnirs dataset, 5 subjects#

fNIRS Finger Tapping

Access recordings and metadata through EEGDash.

Citation: Robert Luke, Eric Larson, Alexandre Gramfort, Macquarie University (—). fNIRS Finger Tapping.

Modality: fnirs Subjects: 5 Recordings: 5 License: CC0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000175

dataset = NM000175(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 = NM000175(cache_dir="./data", subject="01")

Advanced query

dataset = NM000175(
    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{nm000175,
  title = {fNIRS Finger Tapping},
  author = {Robert Luke and Eric Larson and Alexandre Gramfort and Macquarie University},
}

About This Dataset#

BIDS fNIRS Example Dataset

DOI The fNIRS BIDS specification is a work in progress. Expect changes while the BEP is in developement. Example fNIRS dataset that is formated according to the BIDS specification. This repository provides an example dataset demonstrating how a BIDS dataset should be stored. And also demonstrates how to convert measurements obtained using a NIRx device to BIDS using MNE-BIDS (see branches below for script details).

Experiment Description

Dataset Information#

Dataset ID	`NM000175`
Title	fNIRS Finger Tapping
Author (year)	`Luke2024`
Canonical	—
Importable as	`NM000175`, `Luke2024`
Year	—
Authors	Robert Luke, Eric Larson, Alexandre Gramfort, Macquarie University
License	CC0
Citation / DOI	Unknown
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: 5
Tasks: 1

Channels & sampling rate

Channels: 56
Sampling rate (Hz): 7.8125
Duration (hours): 3.808533333333333

Electrode Layout#

Electrode layout — fNIRS · 24 sensors — 24 channels

Dataset Statistics#

Age distribution (n=5, range 20–53 yr)

20253050

Sex distribution

3

2

Female Male Total: 5

Channel counts: 56 ch (n=5 recordings)

Sampling frequencies: 7.8125 Hz (n=5 recordings)

Total recording duration: 3 h 48 min

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 NM000175 class to access this dataset programmatically.

class eegdash.dataset.NM000175(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

fNIRS Finger Tapping

Study:: nm000175 (NeMAR)
Author (year):: Luke2024
Canonical:: —

Also importable as: NM000175, Luke2024.

Modality: fnirs. Subjects: 5; recordings: 5; 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/nm000175 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000175

Examples

>>> from eegdash.dataset import NM000175
>>> dataset = NM000175(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)