DS004819: ieeg dataset, 1 subjects#
Flexible, Scalable, High Channel Count Stereo-Electrode for Recording in the Human Brain
Citation: Keundong Lee, Angelique C. Paulk, Yun Goo Ro, Daniel R. Cleary, Karen J. Tonsfeldt, Yoav Kfir, John Pezaris, Youngbin Tchoe, Jihwan Lee, Andrew M. Bourhis, Ritwik Vatsyayan, Joel R. Martin, Samantha M. Russman, Jimmy C. Yang, Amy Baohan, R. Mark Richardson, Ziv M. Williams, Shelley I. Fried, Hoi Sang U, Ahmed M. Raslan, Sharona Ben-Haim, Eric Halgren, Sydney S. Cash, Shadi. A. Dayeh (—). Flexible, Scalable, High Channel Count Stereo-Electrode for Recording in the Human Brain. 10.18112/openneuro.ds004819.v1.0.0
1-participant iEEG dataset — Flexible, Scalable, High Channel Count Stereo-Electrode for Recording in the Human Brain.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004819
dataset = DS004819(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004819(cache_dir="./data", subject="01")
Advanced query
dataset = DS004819(
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{ds004819,
title = {Flexible, Scalable, High Channel Count Stereo-Electrode for Recording in the Human Brain},
author = {Keundong Lee and Angelique C. Paulk and Yun Goo Ro and Daniel R. Cleary and Karen J. Tonsfeldt and Yoav Kfir and John Pezaris and Youngbin Tchoe and Jihwan Lee and Andrew M. Bourhis and Ritwik Vatsyayan and Joel R. Martin and Samantha M. Russman and Jimmy C. Yang and Amy Baohan and R. Mark Richardson and Ziv M. Williams and Shelley I. Fried and Hoi Sang U and Ahmed M. Raslan and Sharona Ben-Haim and Eric Halgren and Sydney S. Cash and Shadi. A. Dayeh},
doi = {10.18112/openneuro.ds004819.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004819.v1.0.0},
}
About This Dataset#
This project contains the data for the publication Lee et al, “Flexible, Scalable, High Channel Count Stereo-Electrode for Recording in the Human Brain”. It contains the raw and preprocessed (epoched) intracranial EEG (iEEG) data files for multiple species to test novel high resolution micro-stereo-electrodes for recording neural activity in the brain. The data set involves the use of direct electrical stimulation to examine effects of stimulation in the brain.
Data are in the iEEG-BIDS format with binary files and channel maps included in the related derivatives folder.
Cohort#
Dataset Statistics#
Age distribution by gender (n=1, range 1–1 yr, mean 12.5 yr)
Sex composition
Channel counts: 64 ch (n=8 recordings)
Sampling frequencies: 30000.0 Hz (n=8 recordings)
Signal · Electrodes & live trace#
Live trace viewer — sub-SS01MicrosEEG · ses-postimp · task-BaselineandStimRecordingRAWEDF · run-01
Showing one representative recording out of
1 subjects and 8 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.
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 |
Flexible, Scalable, High Channel Count Stereo-Electrode for Recording in the Human Brain |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Keundong Lee, Angelique C. Paulk, Yun Goo Ro, Daniel R. Cleary, Karen J. Tonsfeldt, Yoav Kfir, John Pezaris, Youngbin Tchoe, Jihwan Lee, Andrew M. Bourhis, Ritwik Vatsyayan, Joel R. Martin, Samantha M. Russman, Jimmy C. Yang, Amy Baohan, R. Mark Richardson, Ziv M. Williams, Shelley I. Fried, Hoi Sang U, Ahmed M. Raslan, Sharona Ben-Haim, Eric Halgren, Sydney S. Cash, Shadi. A. Dayeh |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004819,
title = {Flexible, Scalable, High Channel Count Stereo-Electrode for Recording in the Human Brain},
author = {Keundong Lee and Angelique C. Paulk and Yun Goo Ro and Daniel R. Cleary and Karen J. Tonsfeldt and Yoav Kfir and John Pezaris and Youngbin Tchoe and Jihwan Lee and Andrew M. Bourhis and Ritwik Vatsyayan and Joel R. Martin and Samantha M. Russman and Jimmy C. Yang and Amy Baohan and R. Mark Richardson and Ziv M. Williams and Shelley I. Fried and Hoi Sang U and Ahmed M. Raslan and Sharona Ben-Haim and Eric Halgren and Sydney S. Cash and Shadi. A. Dayeh},
doi = {10.18112/openneuro.ds004819.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004819.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004819 · Lee2023eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004819(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Flexible, Scalable, High Channel Count Stereo-Electrode for Recording in the Human Brain
- Study:
ds004819(OpenNeuro)- Author (year):
Lee2023- Canonical:
—
Also importable as:
DS004819,Lee2023.Modality:
ieeg; Experiment type:Clinical/Intervention; Subject type:Surgery. Subjects: 1; recordings: 8; 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/ds004819 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004819 DOI: https://doi.org/10.18112/openneuro.ds004819.v1.0.0 NEMAR citation count: 1
Examples
>>> from eegdash.dataset import DS004819 >>> dataset = DS004819(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/ds004819").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 ds004819 to reproduce the tutorial on this dataset.
Citation
Keundong Lee, Angelique C. Paulk, Yun Goo Ro, Daniel R. Cleary, Karen J. Tonsfeldt, … (n.d.). Flexible, Scalable, High Channel Count Stereo-Electrode for Recording in the Human Brain. 10.18112/openneuro.ds004819.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.ds004819.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset