DS007119: ieeg dataset, 103 subjects#
iEEG_comprehensive_HFA_model_part3
Citation: Keisuke Hatano, Naoto Kuroda, Hiroshi Uda, Kazuki Sakakura, Michael J. Cools, Aimee F. Luat, Shin-Ichiro Osawa, Hitoshi Nemoto, Kazushi Ukishiro, Hidenori Endo, Nobukazu Nakasato, Yutaro Takayama, Keiya Iijima, Masaki Iwasaki, Eishi Asano (—). iEEG_comprehensive_HFA_model_part3. 10.18112/openneuro.ds007119.v1.0.0
103-participant iEEG dataset — iEEG_comprehensive_HFA_model_part3.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS007119
dataset = DS007119(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS007119(cache_dir="./data", subject="01")
Advanced query
dataset = DS007119(
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{ds007119,
title = {iEEG_comprehensive_HFA_model_part3},
author = {Keisuke Hatano and Naoto Kuroda and Hiroshi Uda and Kazuki Sakakura and Michael J. Cools and Aimee F. Luat and Shin-Ichiro Osawa and Hitoshi Nemoto and Kazushi Ukishiro and Hidenori Endo and Nobukazu Nakasato and Yutaro Takayama and Keiya Iijima and Masaki Iwasaki and Eishi Asano},
doi = {10.18112/openneuro.ds007119.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds007119.v1.0.0},
}
About This Dataset#
This dataset contains intracranial EEG data recorded during non-REM sleep and used in Hatano et al. (in press).
Authors:
Keisuke Hatano, Naoto Kuroda, Hiroshi Uda, Kazuki Sakakura, Michael J. Cools, Aimee F. Luat, Shin-Ichiro Osawa, Hitoshi Nemoto, Kazushi Ukishiro, Hidenori Endo, Nobukazu Nakasato, Yutaro Takayama, Keiya Iijima, Masaki Iwasaki, Eishi Asano Funding:
National Institutes of Health (NIH; NS064033 to E.A.); Uehara Memorial Foundation Postdoctoral Fellowship (202441017 to K.H.; 20210301 to H.U.); Japan Society for the Promotion of Science (JP22J23281, JP22KJ0323, and 202560576 to N.K.; 202560628 to H.U.; JP19K09494 and 22K09296 to M.I.)
Cohort#
Dataset Statistics#
Age distribution by gender (n=102, range 1–45 yr, mean 13.7 yr)
Sex composition
Channel counts (ch)
Sampling frequencies: 1000.0 Hz (n=106 recordings)
Total recording duration: 41 h
Signal · Electrodes & live trace#
Live trace viewer — sub-213 · ses-01 · task-sleep · run-01
Showing one representative recording out of
103 subjects and 106 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 · 128 sensors — 128 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 |
iEEG_comprehensive_HFA_model_part3 |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Keisuke Hatano, Naoto Kuroda, Hiroshi Uda, Kazuki Sakakura, Michael J. Cools, Aimee F. Luat, Shin-Ichiro Osawa, Hitoshi Nemoto, Kazushi Ukishiro, Hidenori Endo, Nobukazu Nakasato, Yutaro Takayama, Keiya Iijima, Masaki Iwasaki, Eishi Asano |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds007119,
title = {iEEG_comprehensive_HFA_model_part3},
author = {Keisuke Hatano and Naoto Kuroda and Hiroshi Uda and Kazuki Sakakura and Michael J. Cools and Aimee F. Luat and Shin-Ichiro Osawa and Hitoshi Nemoto and Kazushi Ukishiro and Hidenori Endo and Nobukazu Nakasato and Yutaro Takayama and Keiya Iijima and Masaki Iwasaki and Eishi Asano},
doi = {10.18112/openneuro.ds007119.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds007119.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS007119 · Hatano2025_part3eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS007119(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
iEEG_comprehensive_HFA_model_part3
- Study:
ds007119(OpenNeuro)- Author (year):
Hatano2025_part3- Canonical:
—
Also importable as:
DS007119,Hatano2025_part3.Modality:
ieeg; Experiment type:Sleep; Subject type:Unknown. Subjects: 103; recordings: 106; 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/ds007119 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007119 DOI: https://doi.org/10.18112/openneuro.ds007119.v1.0.0
Examples
>>> from eegdash.dataset import DS007119 >>> dataset = DS007119(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 ds007119 to reproduce the tutorial on this dataset.
Citation
Keisuke Hatano, Naoto Kuroda, Hiroshi Uda, Kazuki Sakakura, Michael J. Cools, … (n.d.). iEEG_comprehensive_HFA_model_part3. 10.18112/openneuro.ds007119.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.ds007119.v1.0.0.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset