DS003505#

VEPCON: Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes

Access recordings and metadata through EEGDash.

Citation: David Pascucci, Sebastien Tourbier, Joan Rue-Queralt, Margherita Carboni, Patric Hagmann, Gijs Plomp (2021). VEPCON: Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes. 10.18112/openneuro.ds003505.v1.1.2

Modality: eeg Subjects: 20 Recordings: 310 License: CC0 Source: openneuro Citations: 5.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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.v1.1.2},
  url = {https://doi.org/10.18112/openneuro.ds003505.v1.1.2},
}

About This Dataset#

VEPCON: Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes

Overview

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.

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.

Dataset Information#

Dataset ID	`DS003505`
Title	VEPCON: Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes
Year	2021
Authors	David Pascucci, Sebastien Tourbier, Joan Rue-Queralt, Margherita Carboni, Patric Hagmann, Gijs Plomp
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds003505.v1.1.2
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: 20
Recordings: 310
Tasks: 2

Channels & sampling rate

Channels: 128
Sampling rate (Hz): 2048.0
Duration (hours): 0.0

API Reference#

Use the DS003505 class to access this dataset programmatically.

class eegdash.dataset.DS003505(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003505. Modality: eeg; Experiment type: Perception; Subject type: Healthy. 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

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/ds003505 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003505

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, 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