NM000254: eeg dataset, 22 subjects#
Naturalistic viewing: An open-access dataset using simultaneous EEG-fMRI
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.
22-participant EEG dataset — Naturalistic viewing: An open-access dataset using simultaneous EEG-fMRI.
Quickstart#
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)
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'])
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.
Cohort#
Dataset Statistics#
Age distribution by gender (n=22, range 23–51 yr, mean 36.4 yr)
Sex composition
Channel counts: 64 ch (n=942 recordings)
Sampling frequencies: 5000.0 Hz (n=942 recordings)
Total recording duration: 108 h
Signal · Electrodes & live trace#
Live trace viewer — sub-01 · ses-01 · task-checker
Showing one representative recording out of
22 subjects and 942 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
Electrode layout — EEG · 61 sensors — 61 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 |
Naturalistic viewing: An open-access dataset using simultaneous EEG-fMRI |
Author (year) |
|
Canonical |
— |
Importable as |
|
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 |
API Reference#
eegdash.datasetEEGDashDatasetNM000254 · Telesford2024eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.NM000254(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
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
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/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: 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 nm000254 to reproduce the tutorial on this dataset.
Citation
Qawi K Telesford, Eduardo Gonzalez-Moreira, Ting Xu, Yiwen Tian, Stanley Colcombe, … (n.d.). Naturalistic viewing: An open-access dataset using simultaneous EEG-fMRI.
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset