ON002718: eeg dataset, 18 subjects#
Face processing EEG dataset for EEGLAB
Citation: Daniel G. Wakeman, Richard N Henson (—). Face processing EEG dataset for EEGLAB. 10.82901/nemar.on002718
18-participant EEG dataset — Face processing EEG dataset for EEGLAB.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON002718
dataset = ON002718(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON002718(cache_dir="./data", subject="01")
Advanced query
dataset = ON002718(
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{on002718,
title = {Face processing EEG dataset for EEGLAB},
author = {Daniel G. Wakeman and Richard N Henson},
doi = {10.82901/nemar.on002718},
url = {https://doi.org/10.82901/nemar.on002718},
}
About This Dataset#
This dataset consists of the MEEG (sMRI+MEG+EEG) portion of the multi-subject, multi-modal face processing dataset (ds000117). This dataset was originally acquired and shared by Daniel Wakeman and Richard Henson (https://pubmed.ncbi.nlm.nih.gov/25977808/). The MEG and EEG data were simultaneously recorded; the sMRI scans were preserved to support M/EEG source localization. Following event log augmentation, reorganization, and HED (v8.0.0) annotation, the EEG data have been repackaged in EEGLAB format.
Overview of the experiment: Eighteen participants completed two recording sessions spaced three months apart – one session recorded fMRI and the other simultaneously recorded MEG and EEG data. During each session, participants performed the same simple perceptual task, responding to presented photographs of famous, unfamiliar, and scrambled faces by pressing one of two keyboard keys to indicate a subjective yes or no decision as to the relative spatial symmetry of the viewed face. Famous faces were feature-matched to unfamiliar faces; half the faces were female. The two sessions (MEEG, fMRI) had different organizations of event timing and presentation because of technological requirements of the respective imaging modalities. Each individual face was presented twice during the session. For half of the presented faces, the second presentation followed immediately after the first. For the other half, the second presentation was delayed by 5-15 face presentations. Preprocessing: Multi-subject, multi-modal (sMRI+EEG) neuroimaging dataset on face processing. Original data described at https://www.nature.com/articles/sdata20151 This is repackaged version of the EEG data in EEGLAB format. The data has gone through minimal preprocessing including (see wh_extracteeg_BIDS.m): - Ignoring fMRI and MEG data (sMRI preserved for EEG source localization) - Extracting EEG channels out of the MEG/EEG fif data - Adding fiducials - Renaming EOG and EKG channels - Extracting events from event channel - Removing spurious events 5, 6, 7, 13, 14, 15, 17, 18 and 19 - Removing spurious event 24 for subject 3 run 4 - Renaming events taking into account button assigned to each subject - Correcting event latencies (events have a shift of 34 ms) - Resampling data to 250 Hz (this is a step that is done because
this dataset is used as tutorial for EEGLAB and need to be lightweight)
This data is a mapping of the original openfmri dataset ds000117 on OpenfMRI, which is no longer
available (although a copy is available in the sourcedata folder of the ds003645 repository). The ds000117 dataset on OpenNeuro contains only 16 subjects. The original OpenfMRI dataset is described at the bottom of this README file https://openneuro.org/datasets/ds000117/versions/1.0.4/file-display/README along with the correspondance with the 16 subjects in ds000117. Note that sub-001 data on OpenfMRI was corrupted so it is not included here. The openneuro dataset ds003645 is similar to this one but also contains MEG data and HED events. Also, it does not have the different runs merged. Import warning Make sure to import the channel locations from the BIDS electrodes.tsv files. The EEGLAB .set files also contain channel locations, although they differ for subjects 8 and 14 because the .set version is wrong and rotated by 90 degrees. When using the EEGLAB EEG BIDS plugin, the default behavior is to import channel locations from BIDS. Data curators: Ramon Martinez, Dung Truong, Scott Makeig, Arnaud Delorme (UCSD, La Jolla, CA, USA)
DOI Introduction:
Cohort#
Dataset Statistics#
Channel counts: 74 ch (n=18 recordings)
Sampling frequencies: 250.0 Hz (n=18 recordings)
Total recording duration: 14 h 50 min
Signal · Electrodes & live trace#
Live trace viewer — sub-002 · task-FaceRecognition
Showing one representative recording out of
18 subjects and 18 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 · 70 sensors — 70 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 |
Face processing EEG dataset for EEGLAB |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Daniel G. Wakeman, Richard N Henson |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on002718,
title = {Face processing EEG dataset for EEGLAB},
author = {Daniel G. Wakeman and Richard N Henson},
doi = {10.82901/nemar.on002718},
url = {https://doi.org/10.82901/nemar.on002718},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON002718(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Face processing EEG dataset for EEGLAB
- Study:
on002718(NeMAR)- Author (year):
nan- Canonical:
—
Also importable as:
ON002718,nan.Modality:
eeg. Subjects: 18; recordings: 18; 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
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/on002718 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on002718 DOI: https://doi.org/10.82901/nemar.on002718
Examples
>>> from eegdash.dataset import ON002718 >>> dataset = ON002718(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 on002718 to reproduce the tutorial on this dataset.
Citation
Daniel G. Wakeman, Richard N Henson (n.d.). Face processing EEG dataset for EEGLAB. 10.82901/nemar.on002718
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on002718.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset