DS007095#

RNS_Epilepsy-iBIDS

Access recordings and metadata through EEGDash.

Citation: Chen Feng, Haoqi Ni, Zhoule Zhu, Hongjie Jiang, Zhe Zheng, Wenjie Ming, Shuang Wang, Kedi Xu, Junming Zhu (2025). RNS_Epilepsy-iBIDS. 10.18112/openneuro.ds007095.v1.0.0

Modality: ieeg Subjects: 8 Recordings: 25153 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007095

dataset = DS007095(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 = DS007095(cache_dir="./data", subject="01")

Advanced query

dataset = DS007095(
    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{ds007095,
  title = {RNS_Epilepsy-iBIDS},
  author = {Chen Feng and Haoqi Ni and Zhoule Zhu and Hongjie Jiang and Zhe Zheng and Wenjie Ming and Shuang Wang and Kedi Xu and Junming Zhu},
  doi = {10.18112/openneuro.ds007095.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007095.v1.0.0},
}

About This Dataset#

Dataset of long-term iEEG invasively recorded in epilepsy patients implanted with responsive neurostimulation system (RNS) We provided a long-term intracranial electroencephalography (iEEG) dataset of 8 epilepsy patients implanted with responsive neurostimulation (RNS) devices. The dataset was constituted by iEEG data recorded from bilateral epileptic lesion areas.

Each recording contains 90 seconds of dual-channel iEEG around each stimulation, 60 seconds before the start of the stimulation, and about 30 seconds after the end of the stimulation. The stimulation markers are contained in the events.tsv files, including the onset and duration for each stimulus. The ieeg.json files contain the electrical stimulation parameters for the current session, which were set by the neurosurgeon during each regular clinical follow-up of epilepsy patients.

The iEEG data were saved in EDF format, stored as the Brain Imaging Data Structure (BIDS), and published on the OpenNeuro. The criterion for including patients in this dataset is to intracranially record the seizure events for more than six months. For each subject, one week is considered as a session, which includes all seizures within a day with high frequency seizure onset during that week.

The dataset can be used to evaluate the alterations of seizure onset pattern during the development of epilepsy, as well as the changes in iEEG characteristics after the electrical stimulation. We have technically validated the dataset through specific signal analysis, such as power spectral analysis, calculation of envelop length, and calculation of phase locking value.

Dataset Information#

Dataset ID	`DS007095`
Title	RNS_Epilepsy-iBIDS
Year	2025
Authors	Chen Feng, Haoqi Ni, Zhoule Zhu, Hongjie Jiang, Zhe Zheng, Wenjie Ming, Shuang Wang, Kedi Xu, Junming Zhu
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007095.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: 8
Recordings: 25153
Tasks: 1

Channels & sampling rate

Channels: 2
Sampling rate (Hz): 200.0
Duration (hours): 0.0

API Reference#

Use the DS007095 class to access this dataset programmatically.

class eegdash.dataset.DS007095(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds007095. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 8; recordings: 6019; 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/ds007095 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007095

Examples

>>> from eegdash.dataset import DS007095
>>> dataset = DS007095(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