DS005398: ieeg dataset, 185 subjects#
Open iEEG Dataset (Pediatric iEEG, Wayne State University and UCLA)
Citation: Yipeng Zhang, Atsuro Daida, Lawrence Liu, Naoto Kuroda, Yuanyi Ding, Shingo Oana, Tonmoy Monsoor, Chenda Duan, Shaun A. Hussain, Joe X Qiao, Noriko Salamon, Aria Fallah, Myung Shin Sim, Raman Sankar, Richard J. Staba, Jerome Engel Jr., Eishi Asano, Vwani Roychowdhury, Hiroki Nariai (20). Open iEEG Dataset (Pediatric iEEG, Wayne State University and UCLA). 10.18112/openneuro.ds005398.v1.1.1
185-participant iEEG dataset — Open iEEG Dataset (Pediatric iEEG, Wayne State University and UCLA).
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS005398
dataset = DS005398(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS005398(cache_dir="./data", subject="01")
Advanced query
dataset = DS005398(
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{ds005398,
title = {Open iEEG Dataset (Pediatric iEEG, Wayne State University and UCLA)},
author = {Yipeng Zhang and Atsuro Daida and Lawrence Liu and Naoto Kuroda and Yuanyi Ding and Shingo Oana and Tonmoy Monsoor and Chenda Duan and Shaun A. Hussain and Joe X Qiao and Noriko Salamon and Aria Fallah and Myung Shin Sim and Raman Sankar and Richard J. Staba and Jerome Engel Jr. and Eishi Asano and Vwani Roychowdhury and Hiroki Nariai},
doi = {10.18112/openneuro.ds005398.v1.1.1},
url = {https://doi.org/10.18112/openneuro.ds005398.v1.1.1},
}
About This Dataset#
This dataset was utilized for the publication of the manuscript by Zhang et al. [1]. A subset of the data has been employed in [2], [3], and [4].
Summary:
This data set comprises the de-identified subjects with interictal iEEG recordings with sleep from University of California Los Angels Mattel Children’s Hospital, and Children’s Hospital of Michigan, Detroit.
Subject-wise information is contained in each folder, including iEEGs collected from 185 subjects during sleep. The channel name and valuables, such as the anatomical label and the resection status, are attached to each folder. The outcome and background information of all the subjects are summarized in ‘paticipant.tsv’ located in the parental directory. Derivatives The processed data for HFO detection and classification are shown in the derivatives/folder. The HFO analysis contains detection from two methods: RMS and MNI detectors.
References: [1] Zhang Y, Daida A, Liu L, Kuroda N, Ding Y, Oana S, Kanai S, Monsoor T, Duan C, Hussain SA, Qiao JX, Salamon N, Fallah A, Sim MS, Sankar R, Staba RJ, Engel J Jr, Asano E, Roychowdhury V, Nariai H. Self-supervised data-driven approach defines pathological high-frequency oscillations in epilepsy. Epilepsia. 2025 Nov;66(11):4434-4450. doi: 10.1111/epi.18545. [2] Monsoor T, Kanai S, Daida A, Kuroda N, Sinha P, Oana S, Zhang Y, Liu L, Singh G, Duan C, Sim MS, Fallah A, Speier W, Asano E, Roychowdhury V, Nariai H. Mini-Seizures: Novel Interictal iEEG Biomarker Capturing Synchronization Network Dynamics at the Epileptogenic Zone. medRxiv. 2025 Feb 2:2025.01.31.25321482. doi: 10.1101/2025.01.31.25321482. [3] Zhang Y, Lu Q, Monsoor T, Hussain SA, Qiao JX, Salamon N, Fallah A, Sim MS, Asano E, Sankar R, Staba RJ, Engel J Jr, Speier W, Roychowdhury V, Nariai H. Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach. Brain Commun. 2021 Nov 3;4(1):fcab267. doi: 10.1093/braincomms/fcab267. [4] Kuroda N, Sonoda M, Miyakoshi M, Nariai H, Jeong JW, Motoi H, Luat AF, Sood S, Asano E. Objective interictal electrophysiology biomarkers optimize prediction of epilepsy surgery outcome. Brain Commun. 2021 Mar 14;3(2):fcab042. doi: 10.1093/braincomms/fcab042.
Cohort#
Dataset Statistics#
Age distribution by gender (n=185, range 2–44 yr, mean 13.4 yr)
Sex composition
Channel counts (ch)
Sampling frequencies (Hz)
Total recording duration: 91 h
Signal · Electrodes & live trace#
Live trace viewer — sub-Detroit049 · ses-01 · task-sleep
Showing one representative recording out of
185 subjects and 185 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 · 63 sensors — 63 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 |
Open iEEG Dataset (Pediatric iEEG, Wayne State University and UCLA) |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Yipeng Zhang, Atsuro Daida, Lawrence Liu, Naoto Kuroda, Yuanyi Ding, Shingo Oana, Tonmoy Monsoor, Chenda Duan, Shaun A. Hussain, Joe X Qiao, Noriko Salamon, Aria Fallah, Myung Shin Sim, Raman Sankar, Richard J. Staba, Jerome Engel Jr., Eishi Asano, Vwani Roychowdhury, Hiroki Nariai |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds005398,
title = {Open iEEG Dataset (Pediatric iEEG, Wayne State University and UCLA)},
author = {Yipeng Zhang and Atsuro Daida and Lawrence Liu and Naoto Kuroda and Yuanyi Ding and Shingo Oana and Tonmoy Monsoor and Chenda Duan and Shaun A. Hussain and Joe X Qiao and Noriko Salamon and Aria Fallah and Myung Shin Sim and Raman Sankar and Richard J. Staba and Jerome Engel Jr. and Eishi Asano and Vwani Roychowdhury and Hiroki Nariai},
doi = {10.18112/openneuro.ds005398.v1.1.1},
url = {https://doi.org/10.18112/openneuro.ds005398.v1.1.1},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005398 · Zhang2024_Open_Pediatric_Wayneeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005398(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Open iEEG Dataset (Pediatric iEEG, Wayne State University and UCLA)
- Study:
ds005398(OpenNeuro)- Author (year):
Zhang2024_Open_Pediatric_Wayne- Canonical:
—
Also importable as:
DS005398,Zhang2024_Open_Pediatric_Wayne.Modality:
ieeg; Experiment type:Clinical/Intervention; Subject type:Epilepsy. Subjects: 185; recordings: 185; 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/ds005398 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005398 DOI: https://doi.org/10.18112/openneuro.ds005398.v1.1.1 NEMAR citation count: 1
Examples
>>> from eegdash.dataset import DS005398 >>> dataset = DS005398(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/ds005398").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 ds005398 to reproduce the tutorial on this dataset.
Citation
Yipeng Zhang, Atsuro Daida, Lawrence Liu, Naoto Kuroda, Yuanyi Ding, … (20). Open iEEG Dataset (Pediatric iEEG, Wayne State University and UCLA). 10.18112/openneuro.ds005398.v1.1.1
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds005398.v1.1.1.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset