DS003505: eeg dataset, 19 subjects#
VEPCON: Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes
Citation: David Pascucci, Sebastien Tourbier, Joan Rue-Queralt, Margherita Carboni, Patric Hagmann, Gijs Plomp (—). VEPCON: Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes. 10.18112/openneuro.ds003505.v1.1.1
19-participant EEG dataset — VEPCON: Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS003505
dataset = DS003505(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS003505(cache_dir="./data", subject="01")
Advanced query
dataset = DS003505(
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{ds003505,
title = {VEPCON: Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes},
author = {David Pascucci and Sebastien Tourbier and Joan Rue-Queralt and Margherita Carboni and Patric Hagmann and Gijs Plomp},
doi = {10.18112/openneuro.ds003505.v1.1.1},
url = {https://doi.org/10.18112/openneuro.ds003505.v1.1.1},
}
About This Dataset#
The multimodal dataset VEPCON follows the BIDS standard and provides raw data of high-density EEG, structural MRI and diffusion weighted images (DWI) recorded in 20 participants.
Visual evoked potentials were recorded while participants discriminated briefly presented faces from scrambled faces (task-faces), or coherently moving stimuli from incoherent ones (task-motion). Note that raw EEG data for sub-05 (for both task-faces and task-motion) and for sub-15 (for task-motion) were discarded because of excessive motion. MRI and DWI were recorded in a separate session from the same participants.
VEPCON also contains data derivatives that follow as close as possible the BIDS derivatives specifications. It includes in particular: pre-processed EEG of single trials in each condition, behavioral measures, structural MRIs, Freesurfer 7.1.1 outputs of defaced MRIs, individual brain parcellations at 5 spatial resolutions (83 to 1015 regions), and corresponding structural connectomes based on fiber count, fiber density, average fractional anisotropy and mean diffusivity maps. In addition, Freesurfer’s outputs include a bem/ folder that contains all files generated by MNE to describe the Boundary Element Model (BEM) based on Freesurfer’s surfaces estimated from the original undefaced structural MRIs. Finally, VEPCON also provides EEG inverse solutions for source imaging based on individual anatomy, and Python and Matlab code for deriving time-series of activity in each brain region, at each parcellation level.
VEPCON: Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes
Overview
We believe this dataset can contribute to multimodal methods development, studying structure-function relations, as well as unimodal optimization of source imaging and graph analysis, among many other possibilities. All code supporting the dataset can be found in the
code/folder.
Cohort#
Dataset Statistics#
Age distribution by gender (n=19, range 20–32 yr, mean 23.4 yr)
Sex composition
Channel counts: 128 ch (n=37 recordings)
Sampling frequencies: 2048.0 Hz (n=37 recordings)
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-faces
Showing one representative recording out of
19 subjects and 37 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 · 128 sensors — 128 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 |
VEPCON: Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
David Pascucci, Sebastien Tourbier, Joan Rue-Queralt, Margherita Carboni, Patric Hagmann, Gijs Plomp |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds003505,
title = {VEPCON: Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes},
author = {David Pascucci and Sebastien Tourbier and Joan Rue-Queralt and Margherita Carboni and Patric Hagmann and Gijs Plomp},
doi = {10.18112/openneuro.ds003505.v1.1.1},
url = {https://doi.org/10.18112/openneuro.ds003505.v1.1.1},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003505 · Pascucci2021eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003505(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
VEPCON: Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes
- Study:
ds003505(OpenNeuro)- Author (year):
Pascucci2021- Canonical:
—
Also importable as:
DS003505,Pascucci2021.Modality:
eeg. Subjects: 19; recordings: 37; 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/ds003505 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003505 DOI: https://doi.org/10.18112/openneuro.ds003505.v1.1.1 NEMAR citation count: 5
Examples
>>> from eegdash.dataset import DS003505 >>> dataset = DS003505(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.pytorchdatasets.load_dataset("EEGDash/ds003505").huggingface
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 ds003505 to reproduce the tutorial on this dataset.
Citation
David Pascucci, Sebastien Tourbier, Joan Rue-Queralt, Margherita Carboni, Patric Hagmann, … (n.d.). VEPCON: Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes. 10.18112/openneuro.ds003505.v1.1.1
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds003505.v1.1.1.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset