DS003848#

Dataset Clinical Epilepsy iEEG to BIDS - RESPect_longterm_iEEG

Access recordings and metadata through EEGDash.

Citation: van Blooijs D., Demuru M., Zweiphenning W, Hermes D., Leijten F., Zijlmans M. (2021). Dataset Clinical Epilepsy iEEG to BIDS - RESPect_longterm_iEEG. 10.18112/openneuro.ds003848.v1.0.3

Modality: ieeg Subjects: 6 Recordings: 184 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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)

View full README

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.

Dataset Information#

Dataset ID	`DS003848`
Title	Dataset Clinical Epilepsy iEEG to BIDS - RESPect_longterm_iEEG
Year	2021
Authors	van Blooijs D., Demuru M., Zweiphenning W, Hermes D., Leijten F., Zijlmans M.
License	CC0
Citation / DOI	10.18112/openneuro.ds003848.v1.0.3
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: 6
Recordings: 184
Tasks: 6

Channels & sampling rate

Channels: 133 (36), 68 (8)
Sampling rate (Hz): 2048.0 (42), 512.0 (2)
Duration (hours): 0.0

API Reference#

Use the DS003848 class to access this dataset programmatically.

class eegdash.dataset.DS003848(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003848. 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

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/ds003848 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003848

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, 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