NM000254: eeg dataset, 22 subjects#

Naturalistic viewing: An open-access dataset using simultaneous EEG-fMRI

Access recordings and metadata through EEGDash.

Citation: Qawi K Telesford, Eduardo Gonzalez-Moreira, Ting Xu, Yiwen Tian, Stanley Colcombe, Jessica Cloud, Brian Edward Russ, Arnaud Falchier, Maximilian Nentwich, Jens Madsen, Lucas Parra, Charles Schroeder, Michael Milham, Alexandre Rosa Franco (—). Naturalistic viewing: An open-access dataset using simultaneous EEG-fMRI.

Modality: eeg Subjects: 22 Recordings: 942 License: — Source: nemar

Metadata: Good (80%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000254

dataset = NM000254(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 = NM000254(cache_dir="./data", subject="01")

Advanced query

dataset = NM000254(
    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{nm000254,
  title = {Naturalistic viewing: An open-access dataset using simultaneous EEG-fMRI},
  author = {Qawi K Telesford and Eduardo Gonzalez-Moreira and Ting Xu and Yiwen Tian and Stanley Colcombe and Jessica Cloud and Brian Edward Russ and Arnaud Falchier and Maximilian Nentwich and Jens Madsen and Lucas Parra and Charles Schroeder and Michael Milham and Alexandre Rosa Franco},
}

About This Dataset#

This dataset is comprised of neuroimaging data collected at the Nathan Kline Institute (NKI). The dataset represents simultaneously collected electroencephalography (EEG) and function magnetic resonance imaging (fMRI) recordings obtained from 22 individuals between the ages of 23 and 51 years-old. EEG data contains 64-channel EEG recordings using a customized Brain Products BrainCapMR consisting of 61 cortical channels, two EOG channels placed below (channel 63) and above (channel 64) the left eye, and one ECG channel (channel 32) placed on the back. This dataset also contains eye tracking and physiological recordings. Eye tracking recordings were collected inside the scanner using EyeLink 1000 (SR Research Ltd.) with eye position and pupil dilation were recorded using an infrared based eye tracker. Physiological recordings were collected using BIOPAC MP150 (BIOPAC Systems, Inc.) using a respiratory transducer belt to monitor breathing. All individuals were consented in accordance and compliance with the Institutional Review Board (IRB) at NKI. Individuals provided demographic information and behavioral data. Behavioral data included participants filling out a survey on their last month of sleep (Pittsburgh Sleep Study), the amount of sleep they had the previous night, and their caffeine intake (if any) before the scan session. The primary goal of this study is to understand the neural underpinnings of brain function evaluating the correlation between electrical activity and hemodynamic fluctuations derived from neuroimaging data.

Dataset Information#

Dataset ID	`NM000254`
Title	Naturalistic viewing: An open-access dataset using simultaneous EEG-fMRI
Author (year)	`Telesford2024`
Canonical	—
Importable as	`NM000254`, `Telesford2024`
Year	—
Authors	Qawi K Telesford, Eduardo Gonzalez-Moreira, Ting Xu, Yiwen Tian, Stanley Colcombe, Jessica Cloud, Brian Edward Russ, Arnaud Falchier, Maximilian Nentwich, Jens Madsen, Lucas Parra, Charles Schroeder, Michael Milham, Alexandre Rosa Franco
License	—
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: 22
Recordings: 942
Tasks: 12

Channels & sampling rate

Channels: 64
Sampling rate (Hz): 5000
Duration (hours): 108.65814155555556

API Reference#

Use the NM000254 class to access this dataset programmatically.

class eegdash.dataset.NM000254(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Naturalistic viewing: An open-access dataset using simultaneous EEG-fMRI

Study:: nm000254 (NeMAR)
Author (year):: Telesford2024
Canonical:: —

Also importable as: NM000254, Telesford2024.

Modality: eeg. Subjects: 22; recordings: 942; tasks: 12.

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/nm000254 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000254

Examples

>>> from eegdash.dataset import NM000254
>>> dataset = NM000254(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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None