DS005169: ieeg dataset, 20 subjects#
Dataset of intracranial EEG during cortical stimulation evoking visual effects
Citation: Andrei Barborica, Felicia Mihai, Laurentiu Tofan, Irina Oane, Ioana Mindruta (20). Dataset of intracranial EEG during cortical stimulation evoking visual effects. 10.18112/openneuro.ds005169.v1.0.0
20-participant iEEG dataset — Dataset of intracranial EEG during cortical stimulation evoking visual effects.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS005169
dataset = DS005169(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS005169(cache_dir="./data", subject="01")
Advanced query
dataset = DS005169(
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{ds005169,
title = {Dataset of intracranial EEG during cortical stimulation evoking visual effects},
author = {Andrei Barborica and Felicia Mihai and Laurentiu Tofan and Irina Oane and Ioana Mindruta},
doi = {10.18112/openneuro.ds005169.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds005169.v1.0.0},
}
About This Dataset#
In this dataset we included iEEG recordings of responses to 115 intracranial high frequency stimulations evoking
visual hallucinations, in 22 patients undergoing stereo-EEG presurgical evaluation for drug-resistant epilepsy.
The dataset contains 21 seconds of iEEG data around each stimulation, 8 seconds before the start of the stimulation,
up to 5 seconds of intracranial stimulation and 8 seconds after the end of the stimulation.
We have used high-frequency bipolar stimulations of different areas of the brain, using alternating polarity biphasic pulses having a duration of 1 ms, at 43.2 Hz or 50 Hz, current intensity between 0.25 to 3 mA, for up to 5 s.
Alternating polarity protocol allows disambiguating neuronal responses time-locked to the stimulation pulses from the artefactual components, according to Barborica et al., 2022 (doi: 10.1002/hbm.25749). It is therefore possible to identify the brain networks underlying the clinical effects, and to create symptom-related activation/connectivity maps.
The contact pair on which stimulation is applied, the current intensity level and evoked effect are specified in the events tsv. The responses are classified in 14 clinical categories: elementary (unstructured flashes of light), plus hallucination (presence of light in different forms or colors overlaying the background vision), minus hallucination (negative elementary phenomena described as scotoma, quadrantanopia, hemianopia or amaurosis), static, dynamic, continuous hallucination, intermittent hallucination, peripheric, central, whole visual field, color, non-color, combined visual symptoms, multimodal hallucinations.
Not all patients in which stimulations evoked visual hallucinations 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 ieeg data. However, they were kept in order to match the number of patients in the companion manuscript.
Contact: andrei.barborica@fizica.unibuc.ro
Cohort#
Dataset Statistics#
Age distribution by gender (n=20, range 18–47 yr, mean 29.5 yr)
Sex composition
Channel counts (ch)
Sampling frequencies: 4096.0 Hz (n=112 recordings)
Total recording duration: 39 min
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · ses-02 · task-dcs
Showing one representative recording out of
20 subjects and 112 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 · 111 sensors — 111 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 of intracranial EEG during cortical stimulation evoking visual effects |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Andrei Barborica, Felicia Mihai, Laurentiu Tofan, Irina Oane, Ioana Mindruta |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds005169,
title = {Dataset of intracranial EEG during cortical stimulation evoking visual effects},
author = {Andrei Barborica and Felicia Mihai and Laurentiu Tofan and Irina Oane and Ioana Mindruta},
doi = {10.18112/openneuro.ds005169.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds005169.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005169 · Barborica2024eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005169(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Dataset of intracranial EEG during cortical stimulation evoking visual effects
- Study:
ds005169(OpenNeuro)- Author (year):
Barborica2024- Canonical:
—
Also importable as:
DS005169,Barborica2024.Modality:
ieeg; Experiment type:Clinical/Intervention; Subject type:Epilepsy. Subjects: 20; recordings: 112; 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/ds005169 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005169 DOI: https://doi.org/10.18112/openneuro.ds005169.v1.0.0 NEMAR citation count: 0
Examples
>>> from eegdash.dataset import DS005169 >>> dataset = DS005169(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/ds005169").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 ds005169 to reproduce the tutorial on this dataset.
Citation
Andrei Barborica, Felicia Mihai, Laurentiu Tofan, Irina Oane, Ioana Mindruta (20). Dataset of intracranial EEG during cortical stimulation evoking visual effects. 10.18112/openneuro.ds005169.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds005169.v1.0.0.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset