DS002799: ieeg dataset, 27 subjects#
Human es-fMRI Resource: Concurrent deep-brain stimulation and whole-brain functional MRI
Citation: Thompson WH*, Nair R*, Oya H*, Esteban O, Shine JM, Petkov CI, Poldrack RA, Howard M, Adolphs R†, *equally contributing, †corresponding author (—). Human es-fMRI Resource: Concurrent deep-brain stimulation and whole-brain functional MRI. 10.18112/openneuro.ds002799.v1.0.4
27-participant iEEG dataset — Human es-fMRI Resource: Concurrent deep-brain stimulation and whole-brain functional MRI.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS002799
dataset = DS002799(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS002799(cache_dir="./data", subject="01")
Advanced query
dataset = DS002799(
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{ds002799,
title = {Human es-fMRI Resource: Concurrent deep-brain stimulation and whole-brain functional MRI},
author = {Thompson WH* and Nair R* and Oya H* and Esteban O and Shine JM and Petkov CI and Poldrack RA and Howard M and Adolphs R† and *equally contributing, †corresponding author},
doi = {10.18112/openneuro.ds002799.v1.0.4},
url = {https://doi.org/10.18112/openneuro.ds002799.v1.0.4},
}
About This Dataset#
Link to published paper for this data resource: https://rdcu.be/b57kz
This collection contains data from 26 human patients who underwent electrical stimulation during functional magnetic resonance imaging (es-fMRI). The patients had medically refractory epilepsy requiring surgically implanted intracranial electrodes in cortical and subcortical locations. One or multiple contacts on these electrodes were stimulated while simultaneously recording BOLD-fMRI activity in a block design. Multiple runs exist for patients with different stimulation sites.
Data is organized in two sessions : Pre-op (pre electrode implantation) and Post-op (post electrode implantation). Raw data is provided in BIDS format and consists of T1s, T2s, resting state scans (pre-op), es-fMRI scans(post-op) , any associated field-maps and stimulation electrode coordinates and stimulation parameters. Pre-processed data (fMRIprep and Freesurfer) is present in the ‘derivatives’ folder.
Notes: 1. Subject IDs 339, 369 and 394 do not have stimulation electrode location data available. 2. Electrodes are in chA-chB format (chA gets leading positive phase of the stimulation). This information is stored in the “channel” file for each stimulation run. 3. In some cases, two distant sites were stimulated simultaneously as indicated by the electrode listed under the appropriate run IDs within the ieeg folders.
Cohort#
Dataset Statistics#
Age distribution (n=26, range 13–59 yr, mean 35.8 yr · sex per subject not reported)
Channel counts (ch)
Signal · Electrodes & live trace#
No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.
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 |
Human es-fMRI Resource: Concurrent deep-brain stimulation and whole-brain functional MRI |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Thompson WH*, Nair R*, Oya H*, Esteban O, Shine JM, Petkov CI, Poldrack RA, Howard M, Adolphs R†, *equally contributing, †corresponding author |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds002799,
title = {Human es-fMRI Resource: Concurrent deep-brain stimulation and whole-brain functional MRI},
author = {Thompson WH* and Nair R* and Oya H* and Esteban O and Shine JM and Petkov CI and Poldrack RA and Howard M and Adolphs R† and *equally contributing, †corresponding author},
doi = {10.18112/openneuro.ds002799.v1.0.4},
url = {https://doi.org/10.18112/openneuro.ds002799.v1.0.4},
}
API Reference#
eegdash.datasetEEGDashDatasetDS002799 · Thompson2024eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS002799(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Human es-fMRI Resource: Concurrent deep-brain stimulation and whole-brain functional MRI
- Study:
ds002799(OpenNeuro)- Author (year):
Thompson2024- Canonical:
—
Also importable as:
DS002799,Thompson2024.Modality:
ieeg; Experiment type:Clinical/Intervention; Subject type:Epilepsy. Subjects: 27; recordings: 16824; 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/ds002799 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002799 DOI: https://doi.org/10.18112/openneuro.ds002799.v1.0.4
Examples
>>> from eegdash.dataset import DS002799 >>> dataset = DS002799(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/ds002799").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 ds002799 to reproduce the tutorial on this dataset.
Citation
Thompson WH, Nair R, Oya H, Esteban O, Shine JM, … (n.d.). Human es-fMRI Resource: Concurrent deep-brain stimulation and whole-brain functional MRI. 10.18112/openneuro.ds002799.v1.0.4
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds002799.v1.0.4.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset