DS005083: ieeg dataset, 61 subjects#

Safety and Accuracy of Stereoelectroencephalography for Pediatric Patients with Prior Craniotomy

Access recordings and metadata through EEGDash.

Citation: Peter H Yang, Nathan Wulfekammer, Amanda V. Jenson, Elliot Neal, Stuart Tomko, John Zempel, Peter Brunner, Sean D McEvoy, Matthew D Smyth, Jarod L Roland (—). Safety and Accuracy of Stereoelectroencephalography for Pediatric Patients with Prior Craniotomy. 10.18112/openneuro.ds005083.v1.0.0

Modality: ieeg Subjects: 61 Recordings: 1357 License: CC0 Source: openneuro

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005083

dataset = DS005083(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 = DS005083(cache_dir="./data", subject="01")

Advanced query

dataset = DS005083(
    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{ds005083,
  title = {Safety and Accuracy of Stereoelectroencephalography for Pediatric Patients with Prior Craniotomy},
  author = {Peter H Yang and Nathan Wulfekammer and Amanda V. Jenson and Elliot Neal and Stuart Tomko and John Zempel and Peter Brunner and Sean D McEvoy and Matthew D Smyth and Jarod L Roland},
  doi = {10.18112/openneuro.ds005083.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005083.v1.0.0},
}

About This Dataset#

BIDS iEEG dataset for the SEEG electrode data used for analysis in the manuscript title “Safety and Accuracy of Stereoelectroencephalography for Pediatric Patients with Prior Craniotomy.” All coordinates are recorded in the individual native post-operative CT imaging space. There was no alignment to other imaging modalities or standardized atlases.

Dataset Information#

Dataset ID	`DS005083`
Title	Safety and Accuracy of Stereoelectroencephalography for Pediatric Patients with Prior Craniotomy
Author (year)	`Yang2024`
Canonical	—
Importable as	`DS005083`, `Yang2024`
Year	—
Authors	Peter H Yang, Nathan Wulfekammer, Amanda V. Jenson, Elliot Neal, Stuart Tomko, John Zempel, Peter Brunner, Sean D McEvoy, Matthew D Smyth, Jarod L Roland
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005083.v1.0.0
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: 61
Recordings: 1357
Tasks: 3

Channels & sampling rate

Channels: 105 (2), 114 (2), 150, 102, 99, 62, 98, 148, 166, 138, 124, 129, 61, 117, 83, 230, 144, 95, 100, 134, 132, 112, 73, 123, 93, 152, 65, 103
Sampling rate (Hz): Varies
Duration (hours): Not calculated

Electrode Layout#

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

Dataset Statistics#

Channel counts (ch)

616265738393959899100102103105112114117123124129132134138144148150152166230

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the DS005083 class to access this dataset programmatically.

class eegdash.dataset.DS005083(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Safety and Accuracy of Stereoelectroencephalography for Pediatric Patients with Prior Craniotomy

Study:: ds005083 (OpenNeuro)
Author (year):: Yang2024
Canonical:: —

Also importable as: DS005083, Yang2024.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Surgery. Subjects: 61; recordings: 1357; tasks: 3.

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/ds005083 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005083 DOI: https://doi.org/10.18112/openneuro.ds005083.v1.0.0

Examples

>>> from eegdash.dataset import DS005083
>>> dataset = DS005083(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)