EEGdash›OpenNeuro›DS004370

Iss. 4370 · 7 subjects · 15 recordings · CC0

Dataset Brief · PRIOS

DS004370: ieeg dataset, 7 subjects#

PRIOS

Citation: van Blooijs D, Blok S, Huiskamp GJM, Leijten FSS (—). PRIOS. 10.18112/openneuro.ds004370.v1.0.2

7-participant iEEG dataset — PRIOS.

Data & curation van Blooijs D · Blok S · Huiskamp GJM · Leijten FSS
Funding EpilepsieNL #17-07 · EpilepsieNL #19-12 · + 1 more

iEEG · 133 (7), 68 (6), 64 (2) ch2048 HzBIDS Brain Imaging Data Structure Specification v1.6.02 tasksSurgeryAnesthesiaClinical/Intervention

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004370

dataset = DS004370(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 = DS004370(cache_dir="./data", subject="01")

Advanced query

dataset = DS004370(
    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{ds004370,
  title = {PRIOS},
  author = {van Blooijs D and Blok S and Huiskamp GJM and Leijten FSS},
  doi = {10.18112/openneuro.ds004370.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds004370.v1.0.2},
}

§ 02Study · The README

About This Dataset#

This dataset consists of 6 patients age 13-53 years old where Cortico-Cortical Evoked Potentials (CCEPs) were recorded with Electro-CorticoGraphy (ECoG) during single pulse electrical stimulation (SPES) in the awake patient for clinical routine (SPES-clinical) and under general propofol-anesthesia (SPES-propofol).

For a detailed description see:

The effect of propofol on local effective brain networks (submitted). D. van Blooijs, S. Blok, G.J.M. Huiskamp, P. van Eijsden, H.G.E. Meijer, F.S.S. Leijten
Dataset description

The study was approved by the Medical Ethical Committee from the UMC Utrecht, the Netherlands.

Contact
- Dorien van Blooijs: D.vanBlooijs@umcutrecht.nl
- Frans Leijten: F.S.S.leijten@umcutrecht.nl
Data organization
View full README
Dataset description

The study was approved by the Medical Ethical Committee from the UMC Utrecht, the Netherlands.

Contact

Dorien van Blooijs: D.vanBlooijs@umcutrecht.nl

Frans Leijten: F.S.S.leijten@umcutrecht.nl

Data organization

This data is organized according to the Brain Imaging Data Structure specification. A community-driven specification for organizing neurophysiology data along with its metadata. For more information on this data specification, see https://bids-specification.readthedocs.io/en/stable/ Each patient has their own folder (e.g., sub-PRIOS01 to sub-PRIOS09) which contains the iEEG recordings data for that patient, as well as the metadata needed to understand the raw data and event timing.

Data are logically grouped in the same BIDS session and stored across runs indicating the day and time point of recording during the monitoring period. We use the optional run key-value pair to specify the day and the start time of the recording (e.g. run-021315, day 2 after implantation, which is day 1 of the monitoring period, at 13:15).

The task key-value pair in long-term iEEG recordings describes the patient’s state during the recording of this file. The task label is “SPESclin“ when these files contain data collected during clinical single pulse electrical stimulation (SPES) and “SPESprop” when these files contain data collected during single pulse electrical stimulation (SPES) in the operating room. Electrode positions were estimated by running Freesurfer on the individual subject MRI scan. All shared electrode positions were converted to MNI305 space using the Freesurfer surface based non-linear transformation. We note that this surface based transformation distorts the dimensions of the grids, but maintains the gyral anatomy.

License

This dataset is made available under the Public Domain Dedication and License CC v1.0, whose full text can be found at https://creativecommons.org/publicdomain/zero/1.0/.

We hope that all users will follow the ODC Attribution/Share-Alike Community Norms (http://www.opendatacommons.org/norms/odc-by-sa/); in particular, while not legally required, we hope that all users of the data will acknowledge by citing the following in any publication.

The effect of propofol on local effective brain networks (submitted). D. van Blooijs, S. Blok, G.J.M. Huiskamp, P. van Eijsden, H.G.E. Meijer, F.S.S. Leijten

Code

Code to analyses these data is available at: UMCU-EpiLAB/umcuEpi_PRIOS

Acknowledgements

We thank all patients for participating in this study.

Funding

Research reported in this publication was supported by EpilepsieNL under Award Number NEF17-07 (DvB) and NEF 19-12 (DvB, SB) and the National Institute of Mental Health of the National Institutes of Health under Award Number R01MH122258 (DvB, the content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health).

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=7, range 13–53 yr, mean 32.4 yr)

1020253550

Female · 3Male · 4

Sex composition

Female
3
Male
4
F : M ratio
0.75 : 1

43% female · n = 7 subjects with reported sex.

Channel counts (ch)

6468133

Sampling frequencies: 2048.0 Hz (n=15 recordings)

Total recording duration: 10 h 12 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 133 (7), 68 (6), 64 (2) ch · iEEG · 2048 Hz · 7 subjects, 15 recordings

Live trace viewer — sub-PRIOS09 · ses-1 · task-SPESclin · run-021433

Showing one representative recording out of 7 subjects and 15 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _ieeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?ieeg=<url>) to inspect it.

Electrode layout — iEEG · 80 sensors — 80 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

§ 05Manifest · BIDS tree

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.DS004370(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Blooijs2022_PRIOS

Canonical—

Importable asDS004370 · Blooijs2022_PRIOS

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.DS004370(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

PRIOS

Study:: ds004370 (OpenNeuro)
Author (year):: Blooijs2022_PRIOS
Canonical:: —

Also importable as: DS004370, Blooijs2022_PRIOS.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Surgery. Subjects: 7; recordings: 15; 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/ds004370 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004370 DOI: https://doi.org/10.18112/openneuro.ds004370.v1.0.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004370
>>> dataset = DS004370(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.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FacePre-bundled mirror at EEGDash/ds004370 · pull with datasets.load_dataset("EEGDash/ds004370").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS004370.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

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 ds004370 to reproduce the tutorial on this dataset.

Citation

van Blooijs D, Blok S, Huiskamp GJM, Leijten FSS (n.d.). PRIOS. 10.18112/openneuro.ds004370.v1.0.2

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds004370.v1.0.2.

Related & sibling datasets

DS004457IEEG · 5 subj DS006065IEEG · 7 subj DS004624IEEG · 3 subj DS004541EEG, FNIRS · 8 subj DS004819IEEG · 1 subj

+ 1 more — see See Also below →

BIDS

BIDS Brain Imaging Data Structure Specification v1.6.0

Sidecars

events · events.json · channels · electrodes

Provenance

CC0 · 10.18112/openneuro.ds004370.v1.0.2

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS004370`
Title	PRIOS
Author (year)	`Blooijs2022_PRIOS`
Canonical	—
Importable as	`DS004370`, `Blooijs2022_PRIOS`
Year	—
Authors	van Blooijs D, Blok S, Huiskamp GJM, Leijten FSS
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004370.v1.0.2
Source links	OpenNeuro \| NeMAR \| Source URL

DS004370: ieeg dataset, 7 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#