DS003848: ieeg dataset, 6 subjects#
Dataset Clinical Epilepsy iEEG to BIDS - RESPect_longterm_iEEG
Citation: van Blooijs D., Demuru M., Zweiphenning W, Hermes D., Leijten F., Zijlmans M. (20). Dataset Clinical Epilepsy iEEG to BIDS - RESPect_longterm_iEEG. 10.18112/openneuro.ds003848.v1.0.3
6-participant iEEG dataset — Dataset Clinical Epilepsy iEEG to BIDS - RESPect_longterm_iEEG.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS003848
dataset = DS003848(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS003848(cache_dir="./data", subject="01")
Advanced query
dataset = DS003848(
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{ds003848,
title = {Dataset Clinical Epilepsy iEEG to BIDS - RESPect_longterm_iEEG},
author = {van Blooijs D. and Demuru M. and Zweiphenning W and Hermes D. and Leijten F. and Zijlmans M.},
doi = {10.18112/openneuro.ds003848.v1.0.3},
url = {https://doi.org/10.18112/openneuro.ds003848.v1.0.3},
}
About This Dataset#
Dataset description
This dataset is part of a bigger dataset of intracranial EEG (iEEG) called RESPect (Registry for Epilepsy Surgery Patients), a dataset recorded at the University Medical Center of Utrecht, the Netherlands.
It consists of 12 patients: six patients recorded intraoperatively using electrocorticography (acute ECoG), six patients with long-term recordings (3 patients recorded with ECoG and 3 patients recorded with stereo-encephalography SEEG). For a detailed description see Demuru M, van Blooijs D, Zweiphenning W, Hermes D, Leijten F, Zijlmans M, on behalf of the RESPect group. “A practical workflow for organizing clinical intraoperative and long-term iEEG data in BIDS“, submitted to NeuroInformatics in 2020.
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-RESP0280) which contains the iEEG recordings data for that patient, as well as the metadata needed to understand the raw data and event timing.
Two different implementation of the BIDS structure were done according to the different type of recordings (i.e. intraoperative or long-term) Intraoperative ECoG Surgery with intraoperative ECoG is composed of three main situations that can be logically grouped into BIDS sessions: * Pre-resection sessions, consisting of all recordings (with different configurations of the grid and strips/depth) carried out before the surgeon has started the planned resection. * Intermediate sessions, consisting of all subsequent recordings performed before any iterative extension of the resection area. * Post-resection sessions, consisting of all the recordings performed after the last resection.
Each situation is labelled with an increasing number starting from 1, indicative of the period in time respective to the surgical resection and a consecutive letter (starting from A) indicative of the position of the grid and strip/depth for a given session.
As an example see patient RESP0280 who had 4 sessions recorded: two pre-resection sessions, one intermediate sessions and one post-resection session. The first session is SITUATION1A consisting of the first recording, then the grid was moved to another position, resulting in SITUATION1B. After that, the surgeon resected part of the brain and then there was another recording(SITUATION2A). Finally the surgeon applied a resection for the last time and the recording after that was defined as SITUATION3A. In long-term recordings, data that are recorded within one monitoring period are logically grouped in the same BIDS session and stored across runs indicating the day and time point of recording in the monitoring period.
If extra electrodes were added/removed during this period, the session was divided into different sessions (e.g. ses-1A and ses-1b). 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. Different tasks have been defined, such as “rest“ when a patient is awake but not doing a specific task, “sleep“ when a patient is sleeping the majority of the file, or “SPESclin“ when the clinical SPES protocol has been performed in this file. Other task definitions can be found in the annotation syntax (UMCU-EpiLAB/umcuEpi_longterm_ieeg_respect_bids). 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 Demuru M, van Blooijs D, Zweiphenning W, Hermes D, Leijten F, Zijlmans M, on behalf of the RESPect group. “A practical workflow for organizing clinical intraoperative and long-term iEEG data in BIDS“, submitted to NeuroInformatics in 2020, in any publications.
Code available at: UMCU-EpiLAB. Acknowledgements We would like to thank the patients for providing their data for this dataset, the RESPect team of University Medical Center of Utrecht, for the acquisition of the dataset.
Please cite Demuru M, van Blooijs D, Zweiphenning W, Hermes D, Leijten F, Zijlmans M, on behalf of the RESPect group. “A practical workflow for organizing clinical intraoperative and long-term iEEG data in BIDS“, submitted to NeuroInformatics in 2020, in any publications.
Cohort#
Dataset Statistics#
Age distribution by gender (n=6, range 14–46 yr, mean 25.3 yr)
Sex composition
Channel counts (ch)
Sampling frequencies (Hz)
Total recording duration: 20 h 16 min
Signal · Electrodes & live trace#
Live trace viewer — sub-RESP0699 · ses-1 · task-sleep · run-020722
Showing one representative recording out of
6 subjects and 22 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 · 110 sensors — 110 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 |
Dataset Clinical Epilepsy iEEG to BIDS - RESPect_longterm_iEEG |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
van Blooijs D., Demuru M., Zweiphenning W, Hermes D., Leijten F., Zijlmans M. |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds003848,
title = {Dataset Clinical Epilepsy iEEG to BIDS - RESPect_longterm_iEEG},
author = {van Blooijs D. and Demuru M. and Zweiphenning W and Hermes D. and Leijten F. and Zijlmans M.},
doi = {10.18112/openneuro.ds003848.v1.0.3},
url = {https://doi.org/10.18112/openneuro.ds003848.v1.0.3},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003848 · Blooijs2021eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003848(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Dataset Clinical Epilepsy iEEG to BIDS - RESPect_longterm_iEEG
- Study:
ds003848(OpenNeuro)- Author (year):
Blooijs2021- Canonical:
—
Also importable as:
DS003848,Blooijs2021.Modality:
ieeg; Experiment type:Clinical/Intervention; Subject type:Epilepsy. Subjects: 6; recordings: 22; tasks: 6.- 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/ds003848 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003848 DOI: https://doi.org/10.18112/openneuro.ds003848.v1.0.3 NEMAR citation count: 1
Examples
>>> from eegdash.dataset import DS003848 >>> dataset = DS003848(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/ds003848").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 ds003848 to reproduce the tutorial on this dataset.
Citation
van Blooijs D., Demuru M., Zweiphenning W, Hermes D., Leijten F., … (20). Dataset Clinical Epilepsy iEEG to BIDS - RESPect_longterm_iEEG. 10.18112/openneuro.ds003848.v1.0.3
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds003848.v1.0.3.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset