DS005398#

Open iEEG Dataset

Access recordings and metadata through EEGDash.

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 (2024). Open iEEG Dataset. 10.18112/openneuro.ds005398.v1.0.1

Modality: ieeg Subjects: 185 Recordings: 930 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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

BibTeX

@dataset{ds005398,
  title = {Open iEEG Dataset},
  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.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005398.v1.0.1},
}

About This Dataset#

This dataset was utilized for the publication of the manuscript by Zhang et al. (in preparation). A subset of the data has been employed in [1] and [2].

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.

Ref

[1] Zhang Y, Lu Q, Monsoor T, et al. Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach. Brain Commun. 2022;4(1):fcab267. doi:10.1093/braincomms/fcab267

[2] Kuroda N, Sonoda M, Miyakoshi M, et al. Objective interictal electrophysiology biomarkers optimize prediction of epilepsy surgery outcome. Brain Commun. 2021;3(2):fcab042. doi:10.1093/braincomms/fcab042

Dataset Information#

Dataset ID	`DS005398`
Title	Open iEEG Dataset
Year	2024
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	doi:10.18112/openneuro.ds005398.v1.0.1
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: 185
Recordings: 930
Tasks: 1

Channels & sampling rate

Channels: 128 (60), 112 (40), 104 (16), 108 (16), 118 (12), 124 (10), 102 (10), 106 (10), 138 (8), 64 (8), 120 (8), 132 (8), 100 (8), 116 (6), 110 (6), 130 (6), 114 (6), 122 (6), 94 (4), 86 (4), 98 (4), 58 (4), 74 (4), 144 (4), 126 (4), 76 (4), 77 (4), 150 (4), 107 (4), 70 (4), 96 (4), 140 (4), 79 (4), 73 (4), 69 (2), 133 (2), 33 (2), 93 (2), 63 (2), 101 (2), 99 (2), 32 (2), 136 (2), 60 (2), 40 (2), 45 (2), 80 (2), 149 (2), 44 (2), 62 (2), 164 (2), 111 (2), 72 (2), 83 (2), 95 (2), 68 (2), 67 (2), 156 (2), 34 (2), 109 (2), 84 (2), 92 (2), 81 (2), 56 (2), 127 (2)
Sampling rate (Hz): 1000.0 (270), 2000.0 (98), 200.0 (2)
Duration (hours): 0.0

API Reference#

Use the DS005398 class to access this dataset programmatically.

class eegdash.dataset.DS005398(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005398. 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

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/ds005398 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005398

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, 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