EEGdash›NeMAR›NM000411

Iss. 411 · 8 subjects · 15 recordings · CC-BY-4.0

Dataset Brief · BrainQuake example SEEG data

NM000411: ieeg dataset, 8 subjects#

BrainQuake example SEEG data: ictal and interictal stereo-EEG from 8 epilepsy patients (Tsinghua Yuquan Hospital)

Access recordings and metadata through EEGDash.

Citation: Kang Wang (2022). BrainQuake example SEEG data: ictal and interictal stereo-EEG from 8 epilepsy patients (Tsinghua Yuquan Hospital). 10.82901/nemar.nm000411

Modality: ieeg Subjects: 8 Recordings: 15 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

8-participant iEEG dataset — BrainQuake example SEEG data: ictal and interictal stereo-EEG from 8 epilepsy patients (Tsinghua Yuquan Hospital).

Data & curation Kang Wang
Senior author Kang Wang
Year 2022 · Distributed via NeMAR
Funding National Key R&D Program of China (2017YFA0205904 to Bo Hong)

iEEG · 150, 148, 142, 147, 109, 140, 158, 153, 145, 163, 104, 107, 123, 102, 155 ch2000 Hz · mixedBIDS 1.10.02 tasks

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000411

dataset = NM000411(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 = NM000411(cache_dir="./data", subject="01")

Advanced query

dataset = NM000411(
    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{nm000411,
  title = {BrainQuake example SEEG data: ictal and interictal stereo-EEG from 8 epilepsy patients (Tsinghua Yuquan Hospital)},
  author = {Kang Wang},
  doi = {10.82901/nemar.nm000411},
  url = {https://doi.org/10.82901/nemar.nm000411},
}

§ 02Study · The README

About This Dataset#

Stereo-EEG (SEEG) recordings from 8 patients with drug-resistant epilepsy, released by Kang Wang

(Tsinghua University) as the example data of the BrainQuake toolbox:

Cai F, Wang K, Zhao T, Wang H, Zhou W, Hong B (2022). BrainQuake: An Open-Source Python Toolbox for the Stereoelectroencephalography Spatiotemporal Analysis. Frontiers in Neuroinformatics 15:773890. https://doi.org/10.3389/fninf.2021.773890

DOI

BrainQuake example SEEG data (Tsinghua Yuquan Hospital)

Source record: Zenodo https://doi.org/10.5281/zenodo.5675459 (“SEEG, MRI, CT for BrainQuake analysis”, CC-BY-4.0, published 2021-11-11; this is the record cited in the paper’s data availability statement).

An earlier Zenodo upload with the same title, https://doi.org/10.5281/zenodo.5494990 (2021-09-09), holds 5 of these patients with the same ictal EDF files (identical MD5) and 5-minute interictal clips; this BIDS dataset uses the later, larger record. The MD5 comparison is in sourcedata/zenodo-5675459/b3w1_provenance.json.

View full README

DOI

BrainQuake example SEEG data (Tsinghua Yuquan Hospital)

Source record: Zenodo https://doi.org/10.5281/zenodo.5675459 (“SEEG, MRI, CT for BrainQuake analysis”, CC-BY-4.0, published 2021-11-11; this is the record cited in the paper’s data availability statement).

An earlier Zenodo upload with the same title, https://doi.org/10.5281/zenodo.5494990 (2021-09-09), holds 5 of these patients with the same ictal EDF files (identical MD5) and 5-minute interictal clips; this BIDS dataset uses the later, larger record. The MD5 comparison is in sourcedata/zenodo-5675459/b3w1_provenance.json.

Contents

sub-01 … sub-08 (source labels S1 … S8, see participants.tsv).
task-ictal: 71-s seizure clip per patient exported by the authors for the BrainQuake ictal module (Epileptogenicity Index). The release does not annotate the seizure onset time within the clip.
task-interictal: interictal segment (up to about 2 h per patient) exported for the interictal module (high-frequency events / HFO detection).
Sampling rate 2000 Hz, physical unit µV, SEEG depth electrodes. Channel labels are kept as released: the ictal clips use plain contact names (A1, A'1), the interictal files use the acquisition system’s labels (POL A3, EEG A1-Ref, …), so the same contact can carry different labels in the two tasks. The interictal files also contain POL ECG, POL EMG1, POL EMG2 (typed ECG/EMG), POL E and POL DC10 (typed MISC; meaning not documented). EDF header prefilter field of the ictal clips: 0.0Hz - 1000.0. Reference and amplifier are not documented.
The interictal files are EDF+D (discontinuous; RecordingType = discontinuous). EDF+ annotations (clinical marks such as IID, EEG Onset, SZ5, asleep, and a few Chinese-language notes, kept verbatim) are listed in *_events.tsv.
Per the paper: recordings were made at the Epilepsy Center of Tsinghua Yuquan Hospital (Beijing) during about two weeks of pre-surgical monitoring; MRI was acquired before and CT after implantation; the study was approved by the hospital’s ethics committee.

Processing

No signal processing. The ictal EDF files are copied byte-for-byte from the release. In each interictal EDF file the first EDF+ annotation (Segment: REC START ...) contained the patient’s name; that text was overwritten with X characters of the same length. Nothing else was changed: headers are identical and all signal bytes are identical (SHA-256 over the non-annotation bytes of every data record, source vs. BIDS; recorded in sourcedata/zenodo-5675459/b3w1_provenance.json). The EDF headers were already de-identified by the authors (patient and recording fields DEIDENTIFIED, start date 01.01.01), so acq_time is n/a.

Channel tables are built from the EDF headers. The release has no electrode coordinates (electrodes.tsv lists contact names with n/a positions).

Not included

S*_mri.nii.gz (pre-implant T1) and S*_ct.nii.gz (post-implant CT): a surface render of these volumes shows reconstructable facial features (no defacing), so they are not redistributed here. They remain available from the CC-BY-4.0 source record. Their MD5 checksums are listed in the provenance file.
Age and sex are not reported in the release or the paper (n/a).

Licence

CC-BY-4.0, as the source record. Please cite the paper and the Zenodo record.

Additional metadata and localisation (added 2026-10-08)

Compiled after the upload from the article, its supplement and the source deposit (each statement names its source). Text and sidecar metadata only; no data file was changed. Electrode types. Depth (SEEG) electrodes; the localisation validation reports an adjacent contact distance of 3.5 mm (95% within 3.5 +/- 1 mm) (doi:10.3389/fninf.2021.773890, Results ‘Electrode localization validation’). Channel names are shaft letter + contact number (e.g. ‘A1’..’K16’; S2 also has primed shafts A’..E’). Localisation method.**Each subject has a preoperative T1 MRI (S*_mri.nii.gz) and a postoperative CT (S*_ct.nii.gz) (https://zenodo.org/records/5494990). The BrainQuake electrode module registers the CT to the FreeSurfer ‘orig’ image with FSL flirt, segments contacts by thresholding/clustering and a centre-of-mass search and labels them anatomically (doi:10.3389/fninf.2021.773890, Methods). No contact coordinates or labels were deposited. **Clinical annotations. The paper validates SOZ predictions against clinician-selected SOZ contacts for five patients (Figs. 8-9), but the per-contact SOZ lists are not published.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000411-blue)](https://doi.org/10.82901/nemar.nm000411) # BrainQuake example SEEG data (Tsinghua Yuquan Hospital) Stereo-EEG (SEEG) recordings from 8 patients with drug-resistant epilepsy, released by Kang Wang (Tsinghua University) as the example data of the BrainQuake toolbox: > Cai F, Wang K, Zhao T, Wang H, Zhou W, Hong B (2022). BrainQuake: An Open-Source Python Toolbox for the > Stereoelectroencephalography Spatiotemporal Analysis. Frontiers in Neuroinformatics 15:773890. > https://doi.org/10.3389/fninf.2021.773890 Source record: Zenodo https://doi.org/10.5281/zenodo.5675459 (“SEEG, MRI, CT for BrainQuake analysis”, CC-BY-4.0, published 2021-11-11; this is the record cited in the paper’s data availability statement). An earlier Zenodo upload with the same title, https://doi.org/10.5281/zenodo.5494990 (2021-09-09), holds 5 of these patients with the same ictal EDF files (identical MD5) and 5-minute interictal clips; this BIDS dataset uses the later, larger record. The MD5 comparison is in sourcedata/zenodo-5675459/b3w1_provenance.json. ## Contents - sub-01 … sub-08 (source labels S1 … S8, see participants.tsv). - task-ictal: 71-s seizure clip per patient exported by the authors for the BrainQuake ictal module

(Epileptogenicity Index). The release does not annotate the seizure onset time within the clip.

task-interictal: interictal segment (up to about 2 h per patient) exported for the interictal module (high-frequency events / HFO detection).
Sampling rate 2000 Hz, physical unit µV, SEEG depth electrodes. Channel labels are kept as released: the ictal clips use plain contact names (A1, A’1), the interictal files use the acquisition system’s labels (POL A3, EEG A1-Ref, …), so the same contact can carry different labels in the two tasks. The interictal files also contain POL ECG, POL EMG1, POL EMG2 (typed ECG/EMG), POL E and POL DC10 (typed MISC; meaning not documented). EDF header prefilter field of the ictal clips: 0.0Hz - 1000.0. Reference and amplifier are not documented.
The interictal files are EDF+D (discontinuous; RecordingType = discontinuous). EDF+ annotations (clinical marks such as IID, EEG Onset, SZ5, asleep, and a few Chinese-language notes, kept verbatim) are listed in *_events.tsv.
Per the paper: recordings were made at the Epilepsy Center of Tsinghua Yuquan Hospital (Beijing) during about two weeks of pre-surgical monitoring; MRI was acquired before and CT after implantation; the study was approved by the hospital’s ethics committee.

## Processing No signal processing. The ictal EDF files are copied byte-for-byte from the release. In each interictal EDF file the first EDF+ annotation (Segment: REC START …) contained the patient’s name; that text was overwritten with X characters of the same length. Nothing else was changed: headers are identical and all signal bytes are identical (SHA-256 over the non-annotation bytes of every data record, source vs. BIDS; recorded in sourcedata/zenodo-5675459/b3w1_provenance.json). The EDF headers were already de-identified by the authors (patient and recording fields DEIDENTIFIED, start date 01.01.01), so acq_time is n/a. Channel tables are built from the EDF headers. The release has no electrode coordinates (electrodes.tsv lists contact names with n/a positions). ## Not included - S*_mri.nii.gz (pre-implant T1) and S*_ct.nii.gz (post-implant CT): a surface render of these volumes

shows reconstructable facial features (no defacing), so they are not redistributed here. They remain available from the CC-BY-4.0 source record. Their MD5 checksums are listed in the provenance file.

Age and sex are not reported in the release or the paper (n/a).

## Licence CC-BY-4.0, as the source record. Please cite the paper and the Zenodo record. ## Additional metadata and localisation (added 2026-10-08) Compiled after the upload from the article, its supplement and the source deposit (each statement names its source). Text and sidecar metadata only; no data file was changed. Electrode types. Depth (SEEG) electrodes; the localisation validation reports an adjacent contact distance of 3.5 mm (95% within 3.5 +/- 1 mm) (doi:10.3389/fninf.2021.773890, Results ‘Electrode localization validation’). Channel names are shaft letter + contact number (e.g. ‘A1’..’K16’; S2 also has primed shafts A’..E’). Localisation method. Each subject has a preoperative T1 MRI (S*_mri.nii.gz) and a postoperative CT (S*_ct.nii.gz) (https://zenodo.org/records/5494990). The BrainQuake electrode module registers the CT to the FreeSurfer ‘orig’ image with FSL flirt, segments contacts by thresholding/clustering and a centre-of-mass search and labels them anatomically (doi:10.3389/fninf.2021.773890, Methods). No contact coordinates or labels were deposited. Clinical annotations. The paper validates SOZ predictions against clinician-selected SOZ contacts for five patients (Figs. 8-9), but the per-contact SOZ lists are not published.

License: CC-BY-4.0

Authors:

Kang Wang

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000411

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts (ch)

102104107109123140142145147148150153155158163

Sampling frequencies (Hz)

19992000.020002000.0

Total recording duration: 14 h 9 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 150, 148, 142, 147, 109, 140, 158, 153, 145, 163, 104, 107, 123, 102, 155 ch · iEEG · 2000 Hz · mixed · 8 subjects, 15 recordings

Live trace viewer — sub-01 · task-ictal

Showing one representative recording out of 8 subjects and 15 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.

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

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

§ 05Manifest · BIDS tree

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000411(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000411

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.NM000411(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

BrainQuake example SEEG data: ictal and interictal stereo-EEG from 8 epilepsy patients (Tsinghua Yuquan Hospital)

Study:: nm000411 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000411.

Modality: ieeg; Subject type: Unknown. Subjects: 8; recordings: 15; tasks: 2.

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/nm000411 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000411 DOI: https://doi.org/10.82901/nemar.nm000411

Examples

>>> from eegdash.dataset import NM000411
>>> dataset = NM000411(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.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000411.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

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 nm000411 to reproduce the tutorial on this dataset.

Citation

Kang Wang (2022). BrainQuake example SEEG data: ictal and interictal stereo-EEG from 8 epilepsy patients (Tsinghua Yuquan Hospital). 10.82901/nemar.nm000411

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.82901/nemar.nm000411.

Related & sibling datasets

NM000387IEEG · 9 subj NM000364IEEG · 7 subj NM000350IEEG · 6 subj NM000397IEEG · 4 subj NM000379IEEG · 6 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.10.0

Sidecars

channels · eeg.json

Provenance

CC-BY-4.0 · 10.82901/nemar.nm000411

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000411`
Title	BrainQuake example SEEG data: ictal and interictal stereo-EEG from 8 epilepsy patients (Tsinghua Yuquan Hospital)
Author (year)	—
Canonical	—
Importable as	`NM000411`
Year	2022
Authors	Kang Wang
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000411
Source links	OpenNeuro \| NeMAR \| Source URL

NM000411: ieeg dataset, 8 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#