ON003645: eeg, meg dataset, 19 subjects#
Face processing MEEG dataset with HED annotation
Citation: Daniel G. Wakeman, Richard N Henson, Dung Truong (curation), Kay Robbins (curation), Scott Makeig (curation), Arno Delorme (curation) (2021). Face processing MEEG dataset with HED annotation. 10.82901/nemar.on003645
19-participant EEG, MEG dataset — Face processing MEEG dataset with HED annotation.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON003645
dataset = ON003645(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON003645(cache_dir="./data", subject="01")
Advanced query
dataset = ON003645(
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{on003645,
title = {Face processing MEEG dataset with HED annotation},
author = {Daniel G. Wakeman and Richard N Henson and Dung Truong (curation) and Kay Robbins (curation) and Scott Makeig (curation) and Arno Delorme (curation)},
doi = {10.82901/nemar.on003645},
url = {https://doi.org/10.82901/nemar.on003645},
}
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:
The EEG preprocessing, which was performed using the wh_extracteeg_BIDS.m located in the code directory, includes the following steps:
* Ignore MRI data except for sMRI.
* Extract EEG channels out of the MEG/EEG fif data
* Add fiducials
* Rename EOG and EKG channels
* Extract events from event channel
* Add button press events!
* Remove spurious event types 5, 6, 7, 13, 14, 15, 17, 18 and 19
* Remove spurious event types 24 for subject 3 run 4
* Correct event latencies (events have a shift of 34 ms)
* Add HED (v8.0.0) event annotations – see Robbins et al. (2021)
* Remove event fields urevent and duration
* Save as EEGLAB .set format
Dung Truong, Ramon Martinez, Scott Makeig, Arnaud Delorme (UCSD, La Jolla, CA, USA), Kay Robbins (UTSA, San Antonio, TX, USA)
DOI Introduction:
Cohort#
Dataset Statistics#
Age distribution by gender (n=18, range 23–37 yr, mean 27.0 yr)
Sex composition
Channel counts (ch)
Sampling frequencies: 1100.0 Hz (n=216 recordings)
Total recording duration: 3 min
Signal · Electrodes & live trace#
Live trace viewer — sub-002 · task-FacePerception · run-1
Showing one representative recording out of
19 subjects and 224 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 MEEG dataset with HED annotation |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
2021 |
Authors |
Daniel G. Wakeman, Richard N Henson, Dung Truong (curation), Kay Robbins (curation), Scott Makeig (curation), Arno Delorme (curation) |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on003645,
title = {Face processing MEEG dataset with HED annotation},
author = {Daniel G. Wakeman and Richard N Henson and Dung Truong (curation) and Kay Robbins (curation) and Scott Makeig (curation) and Arno Delorme (curation)},
doi = {10.82901/nemar.on003645},
url = {https://doi.org/10.82901/nemar.on003645},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON003645(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Face processing MEEG dataset with HED annotation
- Study:
on003645(NeMAR)- Author (year):
nan- Canonical:
—
Also importable as:
ON003645,nan.Modality:
eeg, meg. Subjects: 19; recordings: 224; tasks: 2.- 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/on003645 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on003645 DOI: https://doi.org/10.82901/nemar.on003645
Examples
>>> from eegdash.dataset import ON003645 >>> dataset = ON003645(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 on003645 to reproduce the tutorial on this dataset.
Citation
Daniel G. Wakeman, Richard N Henson, Dung Truong (curation), Kay Robbins (curation), Scott Makeig (curation), … (2021). Face processing MEEG dataset with HED annotation. 10.82901/nemar.on003645
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on003645.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset