DS007095: ieeg dataset, 8 subjects#
RNS_Epilepsy-iBIDS
Citation: Chen Feng, Haoqi Ni, Zhoule Zhu, Hongjie Jiang, Zhe Zheng, Wenjie Ming, Shuang Wang, Kedi Xu, Junming Zhu (—). RNS_Epilepsy-iBIDS. 10.18112/openneuro.ds007095.v1.0.0
8-participant iEEG dataset — RNS_Epilepsy-iBIDS.
Quickstart#
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)
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'])
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.
Cohort#
Dataset Statistics#
Age distribution by gender (n=8, range 24–55 yr, mean 38.2 yr)
Sex composition
Channel counts: 2 ch (n=6019 recordings)
Sampling frequencies: 200.0 Hz (n=6019 recordings)
Total recording duration: 154 h
Signal · Electrodes & live trace#
Live trace viewer — sub-08 · ses-21 · task-seizure · run-01
Showing one representative recording out of
8 subjects and 6019 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _ieeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?ieeg=<url>) to inspect it.
Electrode layout — iEEG · 2 sensors — 2 channels
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
Manifest#
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.
Full dataset metadata table
Dataset ID |
|
Title |
RNS_Epilepsy-iBIDS |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Chen Feng, Haoqi Ni, Zhoule Zhu, Hongjie Jiang, Zhe Zheng, Wenjie Ming, Shuang Wang, Kedi Xu, Junming Zhu |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS007095 · Feng2025eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS007095(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
RNS_Epilepsy-iBIDS
- Study:
ds007095(OpenNeuro)- Author (year):
Feng2025- Canonical:
—
Also importable as:
DS007095,Feng2025.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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand 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 DOI: https://doi.org/10.18112/openneuro.ds007095.v1.0.0
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: 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)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorch
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for ds007095 to reproduce the tutorial on this dataset.
Citation
Chen Feng, Haoqi Ni, Zhoule Zhu, Hongjie Jiang, Zhe Zheng, … (n.d.). RNS_Epilepsy-iBIDS. 10.18112/openneuro.ds007095.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds007095.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset