EEGdash›NeMAR›NM000472

Iss. 472 · 1 subjects · 1 recordings · CC-BY-4.0

Dataset Brief · Zenodo-22150094

NM000472: ieeg dataset, 1 subjects#

Zenodo-22150094: example stereo-EEG (sEEG) recording with one clinically confirmed seizure

Access recordings and metadata through EEGDash.

Citation: AI-is-out-there (19). Zenodo-22150094: example stereo-EEG (sEEG) recording with one clinically confirmed seizure. 10.82901/nemar.nm000472

Modality: ieeg Subjects: 1 Recordings: 1 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

1-participant iEEG dataset — Zenodo-22150094: example stereo-EEG (sEEG) recording with one clinically confirmed seizure.

Data & curation AI-is-out-there
Senior author AI-is-out-there
Year 19 · Distributed via NeMAR

iEEG · 102 ch256 HzBIDS 1.10.0Task · seizure

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 NM000472

dataset = NM000472(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 = NM000472(cache_dir="./data", subject="01")

Advanced query

dataset = NM000472(
    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{nm000472,
  title = {Zenodo-22150094: example stereo-EEG (sEEG) recording with one clinically confirmed seizure},
  author = {AI-is-out-there},
  doi = {10.82901/nemar.nm000472},
  url = {https://doi.org/10.82901/nemar.nm000472},
}

§ 02Study · The README

About This Dataset#

Stereo-EEG (sEEG) recording of one patient during clinical intracranial monitoring, released on Zenodo by

“AI-is-out-there” as “example sEEG (stereo EEG is a specific subtype of intracranial EEG + MRI) with clinical confirmed one seizure” (doi:10.5281/zenodo.22150094, CC-BY-4.0, version 2 (MRI added)). The record’s whole description reads: “‘sEEG-HFOs-8.edf’: seizure 17:27:11, clinicaly confirmed 17:27:14 / MRI T1 and T2”. No article, protocol, ethics statement or demographic information is given in the record; this README does not add any.

16d2be49ccae55379531286de94768f6321ff3ac71860b1c65e41b2a96b502c5). 102 signals (100 sEEG contacts labelled EEG <electrode><contact>,

2 marker inputs MKR1+/MKR2+) at 256 Hz, 3.92 h (14095 one-second records), physical range +-3200 uV, 16-bit. The EDF identification fields are the depositor’s placeholders (patient_2_3_17 X 01-JAN-1999 name, Startdate 01-JAN-2019 01.00.00 X X X), so the recording date/time in the header is not the real one.

DOI

Example stereo-EEG recording with one clinically confirmed seizure (Zenodo 22150094)

Electrodes (12): R, FP, FD, PM, CC, SA, PA, CR’, CC’, PM’, SA’, PA’. Names ending in ' follow the usual sEEG convention for the contralateral hemisphere, but the record does not state which hemisphere is which, so no hemisphere is asserted here.
events.tsv: the 3 EDF+ annotations of the file, verbatim (Russian, Windows-1251 in the file, UTF-8 here) with an English rendering added by the converter. They mark the clinical onset and a seizure with bilateral tonic-clonic evolution (“приступ + БТКП”). - 10392.734 s: где тут начало? - 10396.445 s: приступ + БТКП - 10399.469 s: клиника
Not included: MRI-with-electrodes.zip (DICOM T1 and T2 with implanted electrodes, 432 slices). It is not defaced, so it is withheld from this redistribution; it is available from the Zenodo record. No electrode coordinates are therefore released (electrodes.tsv has n/a coordinates).
sourcedata/zenodo-22150094/: the Zenodo record metadata (JSON).

Provenance

Converted by the ieeg-nemar-20261005 campaign (lane o5c-B) with code/o5c_B_conv_generic12.py; the EDF data records are unchanged. code/conversion_report.json lists the header fields, the annotation texts and the checksums.

Note: the EDF patient-code and patient-name header fields were set to X on 2026-10-12 as part of header de-identification; no field values are reproduced here.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000472-blue)](https://doi.org/10.82901/nemar.nm000472) # Example stereo-EEG recording with one clinically confirmed seizure (Zenodo 22150094) Stereo-EEG (sEEG) recording of one patient during clinical intracranial monitoring, released on Zenodo by “AI-is-out-there” as “example sEEG (stereo EEG is a specific subtype of intracranial EEG + MRI) with clinical confirmed one seizure” (doi:10.5281/zenodo.22150094, CC-BY-4.0, version 2 (MRI added)). The record’s whole description reads: “‘sEEG-HFOs-8.edf’: seizure 17:27:11, clinicaly confirmed 17:27:14 / MRI T1 and T2”. No article, protocol, ethics statement or demographic information is given in the record; this README does not add any. ## Content - sub-01/ieeg/sub-01_task-seizure_ieeg.edf: the released EDF+ file, byte-identical copy (sha256

16d2be49ccae55379531286de94768f6321ff3ac71860b1c65e41b2a96b502c5). 102 signals (100 sEEG contacts labelled EEG <electrode><contact>, 2 marker inputs MKR1+/MKR2+) at 256 Hz, 3.92 h (14095 one-second records), physical range +-3200 uV, 16-bit. The EDF identification fields are the depositor’s placeholders (patient_2_3_17 X 01-JAN-1999 name, Startdate 01-JAN-2019 01.00.00 X X X), so the recording date/time in the header is not the real one.

Electrodes (12): R, FP, FD, PM, CC, SA, PA, CR’, CC’, PM’, SA’, PA’. Names ending in ‘ follow the usual sEEG convention for the contralateral hemisphere, but the record does not state which hemisphere is which, so no hemisphere is asserted here.
events.tsv: the 3 EDF+ annotations of the file, verbatim (Russian, Windows-1251 in the file, UTF-8 here) with an English rendering added by the converter. They mark the clinical onset and a seizure with bilateral tonic-clonic evolution (“приступ + БТКП”). - 10392.734 s: где тут начало? - 10396.445 s: приступ + БТКП - 10399.469 s: клиника
Not included: MRI-with-electrodes.zip (DICOM T1 and T2 with implanted electrodes, 432 slices). It is not defaced, so it is withheld from this redistribution; it is available from the Zenodo record. No electrode coordinates are therefore released (electrodes.tsv has n/a coordinates).
sourcedata/zenodo-22150094/: the Zenodo record metadata (JSON).

## Provenance Converted by the ieeg-nemar-20261005 campaign (lane o5c-B) with code/o5c_B_conv_generic12.py; the EDF data records are unchanged. code/conversion_report.json lists the header fields, the annotation texts and the checksums. Note: the EDF patient-code and patient-name header fields were set to X on 2026-10-12 as part of header de-identification; no field values are reproduced here.

License: CC-BY-4.0

Authors:

AI-is-out-there

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000472

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 102 ch (n=1 recordings)

Sampling frequencies: 256.0 Hz (n=1 recordings)

Total recording duration: 3 h 54 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 102 ch · iEEG · 256 Hz · 1 subjects, 1 recordings

Live trace viewer — sub-01 · task-seizure

Showing one representative recording out of 1 subjects and 1 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.NM000472(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000472

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

class eegdash.dataset.NM000472(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Zenodo-22150094: example stereo-EEG (sEEG) recording with one clinically confirmed seizure

Study:: nm000472 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000472.

Modality: ieeg; Subject type: Unknown. Subjects: 1; recordings: 1; 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/nm000472 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000472 DOI: https://doi.org/10.82901/nemar.nm000472

Examples

>>> from eegdash.dataset import NM000472
>>> dataset = NM000472(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 — NM000472.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 nm000472 to reproduce the tutorial on this dataset.

Citation

AI-is-out-there (19). Zenodo-22150094: example stereo-EEG (sEEG) recording with one clinically confirmed seizure. 10.82901/nemar.nm000472

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000472.

Related & sibling datasets

NM000469IEEG · 1 subj NM000462IEEG · 3 subj NM000400IEEG · 7 subj NM000437IEEG · 5 subj NM000409IEEG · 8 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.10.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · 10.82901/nemar.nm000472

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000472`
Title	Zenodo-22150094: example stereo-EEG (sEEG) recording with one clinically confirmed seizure
Author (year)	—
Canonical	—
Importable as	`NM000472`
Year	19
Authors	AI-is-out-there
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000472
Source links	OpenNeuro \| NeMAR \| Source URL

NM000472: ieeg dataset, 1 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#