DS007118: ieeg dataset, 65 subjects#

iEEG_comprehensive_HFA_model_part1

Access recordings and metadata through EEGDash.

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 (2025). iEEG_comprehensive_HFA_model_part1. 10.18112/openneuro.ds007118.v1.0.0

Modality: ieeg Subjects: 65 Recordings: 82 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007118

dataset = DS007118(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS007118(cache_dir="./data", subject="01")

Advanced query

dataset = DS007118(
    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{ds007118,
  title = {iEEG_comprehensive_HFA_model_part1},
  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.ds007118.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007118.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.)

Dataset Information#

Dataset ID

DS007118

Title

iEEG_comprehensive_HFA_model_part1

Author (year)

Hatano2025_part1

Canonical

Hatano

Importable as

DS007118, Hatano2025_part1, Hatano

Year

2025

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

doi:10.18112/openneuro.ds007118.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL

Copy-paste BibTeX
@dataset{ds007118,
  title = {iEEG_comprehensive_HFA_model_part1},
  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.ds007118.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007118.v1.0.0},
}

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings
  • Subjects: 65

  • Recordings: 82

  • Tasks: 1

Channels & sampling rate
  • Channels: 128 (21), 112 (17), 124 (6), 102 (5), 108 (4), 120 (4), 68 (3), 116 (3), 138 (3), 118 (3), 106 (2), 144 (2), 64 (2), 122, 114, 74, 94, 36, 132, 58

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 44.215

Tags
  • Pathology: Not specified

  • Modality: Sleep

  • Type: Sleep

Files & format
  • Size on disk: 33.8 GB

  • File count: 82

  • Format: BIDS

License & citation
  • License: CC0

  • DOI: doi:10.18112/openneuro.ds007118.v1.0.0

Provenance

API Reference#

Use the DS007118 class to access this dataset programmatically.

class eegdash.dataset.DS007118(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

iEEG_comprehensive_HFA_model_part1

Study:

ds007118 (OpenNeuro)

Author (year):

Hatano2025_part1

Canonical:

Hatano

Also importable as: DS007118, Hatano2025_part1, Hatano.

Modality: ieeg; Experiment type: Sleep; Subject type: Unknown. Subjects: 65; recordings: 82; 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

query#

Merged query with the dataset filter applied.

Type:

dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007118 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007118 DOI: https://doi.org/10.18112/openneuro.ds007118.v1.0.0

Examples

>>> from eegdash.dataset import DS007118
>>> dataset = DS007118(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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:
  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#