DS004551#

iEEG on children during slow wave sleep

Access recordings and metadata through EEGDash.

Citation: Kazuki Sakakura, Naoto Kuroda, Masaki Sonoda, Takumi Mitsuhashi, Ethan Firestone, Aimee F. Luat, Neena I. Marupudi, Sandeep Sood, Eishi Asano (2023). iEEG on children during slow wave sleep. 10.18112/openneuro.ds004551.v1.0.6

Modality: ieeg Subjects: 114 Recordings: 504 License: CC0 Source: openneuro Citations: 3.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004551

dataset = DS004551(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = DS004551(cache_dir="./data", subject="01")

Advanced query

dataset = DS004551(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds004551,
  title = {iEEG on children during slow wave sleep},
  author = {Kazuki Sakakura and Naoto Kuroda and Masaki Sonoda and Takumi Mitsuhashi and Ethan Firestone and Aimee F. Luat and Neena I. Marupudi and Sandeep Sood and Eishi Asano},
  doi = {10.18112/openneuro.ds004551.v1.0.6},
  url = {https://doi.org/10.18112/openneuro.ds004551.v1.0.6},
}

About This Dataset#

This dataset was curated for publication as part of the manuscript in Sakakura et al. (in preparation). It contains iEEGs collected from 114 individuals during slow wave sleep. The available Matlab code can be found at kaz1126/MI_HFO. The iEEG coordinate system employed in this dataset is MNI305.

Dataset Information#

Dataset ID	`DS004551`
Title	iEEG on children during slow wave sleep
Year	2023
Authors	Kazuki Sakakura, Naoto Kuroda, Masaki Sonoda, Takumi Mitsuhashi, Ethan Firestone, Aimee F. Luat, Neena I. Marupudi, Sandeep Sood, Eishi Asano
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004551.v1.0.6
Source links	OpenNeuro \| NeMAR \| Source URL

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: 114
Recordings: 504
Tasks: 1

Channels & sampling rate

Channels: 128 (164), 112 (10), 118 (6), 138 (6), 142 (4), 108 (4), 124 (4), 110 (4), 134 (4), 104 (4), 144 (4), 130 (4), 102 (4), 122 (4), 148 (4), 120 (2), 58 (2), 132 (2), 126 (2), 136 (2), 116 (2), 84 (2), 146 (2), 106 (2), 96 (2)
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS004551 class to access this dataset programmatically.

class eegdash.dataset.DS004551(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004551. Modality: ieeg; Experiment type: Sleep; Subject type: Unknown. Subjects: 114; recordings: 125; 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/ds004551 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004551

Examples

>>> from eegdash.dataset import DS004551
>>> dataset = DS004551(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