DS004819#

Flexible, Scalable, High Channel Count Stereo-Electrode for Recording in the Human Brain

Access recordings and metadata through EEGDash.

Citation: Keundong Lee, Angelique C. Paulk, Yun Goo Ro, Daniel R. Cleary, Karen J. Tonsfeldt, Yoav Kfir, John Pezaris, Youngbin Tchoe, Jihwan Lee, Andrew M. Bourhis, Ritwik Vatsyayan, Joel R. Martin, Samantha M. Russman, Jimmy C. Yang, Amy Baohan, R. Mark Richardson, Ziv M. Williams, Shelley I. Fried, Hoi Sang U, Ahmed M. Raslan, Sharona Ben-Haim, Eric Halgren, Sydney S. Cash, Shadi. A. Dayeh (2023). Flexible, Scalable, High Channel Count Stereo-Electrode for Recording in the Human Brain. 10.18112/openneuro.ds004819.v1.0.0

Modality: ieeg Subjects: 1 Recordings: 30 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004819

dataset = DS004819(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 = DS004819(cache_dir="./data", subject="01")

Advanced query

dataset = DS004819(
    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{ds004819,
  title = {Flexible, Scalable, High Channel Count Stereo-Electrode for Recording in the Human Brain},
  author = {Keundong Lee and Angelique C. Paulk and Yun Goo Ro and Daniel R. Cleary and Karen J. Tonsfeldt and Yoav Kfir and John Pezaris and Youngbin Tchoe and Jihwan Lee and Andrew M. Bourhis and Ritwik Vatsyayan and Joel R. Martin and Samantha M. Russman and Jimmy C. Yang and Amy Baohan and R. Mark Richardson and Ziv M. Williams and Shelley I. Fried and Hoi Sang U and Ahmed M. Raslan and Sharona Ben-Haim and Eric Halgren and Sydney S. Cash and Shadi. A. Dayeh},
  doi = {10.18112/openneuro.ds004819.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004819.v1.0.0},
}

About This Dataset#

This project contains the data for the publication Lee et al, “Flexible, Scalable, High Channel Count Stereo-Electrode for Recording in the Human Brain”. It contains the raw and preprocessed (epoched) intracranial EEG (iEEG) data files for multiple species to test novel high resolution micro-stereo-electrodes for recording neural activity in the brain. The data set involves the use of direct electrical stimulation to examine effects of stimulation in the brain.

Data are in the iEEG-BIDS format with binary files and channel maps included in the related derivatives folder.

Dataset Information#

Dataset ID	`DS004819`
Title	Flexible, Scalable, High Channel Count Stereo-Electrode for Recording in the Human Brain
Year	2023
Authors	Keundong Lee, Angelique C. Paulk, Yun Goo Ro, Daniel R. Cleary, Karen J. Tonsfeldt, Yoav Kfir, John Pezaris, Youngbin Tchoe, Jihwan Lee, Andrew M. Bourhis, Ritwik Vatsyayan, Joel R. Martin, Samantha M. Russman, Jimmy C. Yang, Amy Baohan, R. Mark Richardson, Ziv M. Williams, Shelley I. Fried, Hoi Sang U, Ahmed M. Raslan, Sharona Ben-Haim, Eric Halgren, Sydney S. Cash, Shadi. A. Dayeh
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004819.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: 1
Recordings: 30
Tasks: —

Channels & sampling rate

Channels: 64
Sampling rate (Hz): 30000.0
Duration (hours): 0.0

API Reference#

Use the DS004819 class to access this dataset programmatically.

class eegdash.dataset.DS004819(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004819. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 1; recordings: 8; 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/ds004819 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004819

Examples

>>> from eegdash.dataset import DS004819
>>> dataset = DS004819(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