ON006519: ieeg dataset, 21 subjects#
Dataset of intracranial EEG during cortical stimulations evoking negative motor responses
Access recordings and metadata through EEGDash.
Citation: Andrei Barborica, Cristina Ghita, Laurentiu Tofan, Irina Oane, Ioana Mindruta (—). Dataset of intracranial EEG during cortical stimulations evoking negative motor responses. 10.82901/nemar.on006519
Modality: ieeg Subjects: 21 Recordings: 35 License: CC0 Source: nemar
Metadata: Good (80%)
21-participant iEEG dataset — Dataset of intracranial EEG during cortical stimulations evoking negative motor responses.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON006519
dataset = ON006519(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON006519(cache_dir="./data", subject="01")
Advanced query
dataset = ON006519(
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{on006519,
title = {Dataset of intracranial EEG during cortical stimulations evoking negative motor responses},
author = {Andrei Barborica and Cristina Ghita and Laurentiu Tofan and Irina Oane and Ioana Mindruta},
doi = {10.82901/nemar.on006519},
url = {https://doi.org/10.82901/nemar.on006519},
}
About This Dataset#
In this dataset we included iEEG recordings of responses to 41 intracranial high frequency stimulations evoking
negative motor responses, in 23 patients undergoing stereo-EEG presurgical evaluation for drug-resistant epilepsy.
The dataset contains 24 seconds of iEEG data around each stimulation, 9-10 seconds before the start of the stimulation,
up to 5 seconds of intracranial stimulation and 9-10 seconds after the end of the stimulation. Each recording contains two 5 second epochs, pre-stimulation (used as baseline in the connectivity analysis) and post-stimulation.
We have used high-frequency bipolar stimulations of different areas of the brain, using biphasic pulses having a duration of 1 ms, at a frequency of 43.2 Hz (alternating polarity) or 50 Hz (non-alternating), current intensity between 0.25 to 3 mA, for up to 5 s.
The contact pair on which stimulation is applied, the current intensity level and evoked effect are specified in the events tsv.
Not all patients in which stimulations evoked negative motor responses met the inclusion criteria for network analysis that requires running the freesurfer pipeline, for instance patients having prior resections, therefore there are subjects that do not contain anatomy data and are not included in the dataset. However, they are included in the numbering of patients to match the table in the companion manuscript.
Contact: andrei.barborica@fizica.unibuc.ro
Cohort#
Dataset Statistics#
Age distribution (n=23, range 9–54 yr, mean 29.0 yr · sex per subject not reported)
Sex composition
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 |
Dataset of intracranial EEG during cortical stimulations evoking negative motor responses |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Andrei Barborica, Cristina Ghita, Laurentiu Tofan, Irina Oane, Ioana Mindruta |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on006519,
title = {Dataset of intracranial EEG during cortical stimulations evoking negative motor responses},
author = {Andrei Barborica and Cristina Ghita and Laurentiu Tofan and Irina Oane and Ioana Mindruta},
doi = {10.82901/nemar.on006519},
url = {https://doi.org/10.82901/nemar.on006519},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON006519(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Dataset of intracranial EEG during cortical stimulations evoking negative motor responses
- Study:
on006519(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
ON006519.Modality:
ieeg; Subject type:Unknown. Subjects: 21; recordings: 35; tasks: 1.- 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/on006519 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on006519 DOI: https://doi.org/10.82901/nemar.on006519
Examples
>>> from eegdash.dataset import ON006519 >>> dataset = ON006519(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.pytorch
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 on006519 to reproduce the tutorial on this dataset.
Citation
Andrei Barborica, Cristina Ghita, Laurentiu Tofan, Irina Oane, Ioana Mindruta (n.d.). Dataset of intracranial EEG during cortical stimulations evoking negative motor responses. 10.82901/nemar.on006519
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on006519.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset