EEGdash›NeMAR›NM000182

Iss. 182 · 24 subjects · 257 recordings · CC0

Dataset Brief · Multicenter iEEG dataset for classification of graphoelements…

NM000182: ieeg dataset, 24 subjects#

Multicenter iEEG dataset for classification of graphoelements and artifactual signals (MAYO)

Access recordings and metadata through EEGDash.

Citation: Petr Nejedly, Vaclav Kremen, Vladimir Sladky, Jan Cimbalnik, Petr Klimes, Filip Plesinger, Filip Mivalt, Vojtech Travnicek, Ivo Viscor, Martin Pail, Josef Halamek, Benjamin Brinkmann, Milan Brazdil, Pavel Jurak, Gregory Worrell (20). Multicenter iEEG dataset for classification of graphoelements and artifactual signals (MAYO). 10.82901/nemar.nm000182

Modality: ieeg Subjects: 24 Recordings: 257 License: CC0 Source: nemar

Metadata: Complete (100%)

24-participant iEEG dataset — Multicenter iEEG dataset for classification of graphoelements and artifactual signals (MAYO).

Data & curation Petr Nejedly · Vaclav Kremen · Vladimir Sladky · Jan Cimbalnik · Petr Klimes · Filip Plesinger · …
Senior author Gregory Worrell
Year 20 · Distributed via NeMAR

iEEG · 1 ch5000 HzBIDS 1.10.1Task · MachineLearningEEG

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 NM000182

dataset = NM000182(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 = NM000182(cache_dir="./data", subject="01")

Advanced query

dataset = NM000182(
    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{nm000182,
  title = {Multicenter iEEG dataset for classification of graphoelements and artifactual signals (MAYO)},
  author = {Petr Nejedly and Vaclav Kremen and Vladimir Sladky and Jan Cimbalnik and Petr Klimes and Filip Plesinger and Filip Mivalt and Vojtech Travnicek and Ivo Viscor and Martin Pail and Josef Halamek and Benjamin Brinkmann and Milan Brazdil and Pavel Jurak and Gregory Worrell},
  doi = {10.82901/nemar.nm000182},
  url = {https://doi.org/10.82901/nemar.nm000182},
}

§ 02Study · The README

About This Dataset#

Nejedly2020 multicenter iEEG graphoelement clips (MAYO)

This BIDS dataset is converted from the public segmented .mat archives released with Nejedly et al. 2020, Scientific Data: https://doi.org/10.1038/s41597-020-0532-5 The source data are independent 3 s single-channel iEEG clips. The original authors also released MEF3 BIDS archives for reviewer-requested compatibility, but those contain the same data as the .mat clips. This conversion therefore uses the segmented archives directly.

Each BIDS run contains clips from one subject and one source SEEG contact,

concatenated at 5000 Hz. Events mark the 3 s clip boundaries and preserve the original label, segment_id, anatomy, reviewer_id, SOZ flag, and electrode type.

The runs are not continuous clinical recordings and should not be interpreted as real time-contiguous data across clip boundaries.

Subject labels preserve the original NEMAR/OpenNeuro style (for example sub-000) because this is a single-site dataset.

Electrodes

Patient-specific electrode coordinates were not released with the segmented dataset. Therefore electrodes.tsv keeps x/y/z as n/a while preserving contact names, material, and manufacturer. Anatomy and SOZ labels are preserved per clip in events.tsv.

NEMAR Metadata#

Nejedly2020 multicenter iEEG graphoelement clips (MAYO) This BIDS dataset is converted from the public segmented .mat archives released with Nejedly et al. 2020, Scientific Data: https://doi.org/10.1038/s41597-020-0532-5 The source data are independent 3 s single-channel iEEG clips. The original authors also released MEF3 BIDS archives for reviewer-requested compatibility, but those contain the same data as the .mat clips. This conversion therefore uses the segmented archives directly. Layout —— Each BIDS run contains clips from one subject and one source SEEG contact, concatenated at 5000 Hz. Events mark the 3 s clip boundaries and preserve the original label, segment_id, anatomy, reviewer_id, SOZ flag, and electrode type. The runs are not continuous clinical recordings and should not be interpreted as real time-contiguous data across clip boundaries. Subject labels preserve the original NEMAR/OpenNeuro style (for example sub-000) because this is a single-site dataset. Electrodes ———- Patient-specific electrode coordinates were not released with the segmented dataset. Therefore electrodes.tsv keeps x/y/z as n/a while preserving contact names, material, and manufacturer. Anatomy and SOZ labels are preserved per clip in events.tsv.

License: CC0

Authors:

Petr Nejedly
Vaclav Kremen
Vladimir Sladky
Jan Cimbalnik
Petr Klimes
… and 10 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000182

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 1 ch (n=257 recordings)

Sampling frequencies: 5000.0 Hz (n=257 recordings)

Total recording duration: 129 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 1 ch · iEEG · 5000 Hz · 24 subjects, 257 recordings

Live trace viewer — sub-015 · task-MachineLearningEEG · run-004

Showing one representative recording out of 24 subjects and 257 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.NM000182(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000182

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

class eegdash.dataset.NM000182(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Multicenter iEEG dataset for classification of graphoelements and artifactual signals (MAYO)

Study:: nm000182 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000182.

Modality: ieeg; Subject type: Unknown. Subjects: 24; recordings: 257; 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/nm000182 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000182 DOI: https://doi.org/10.82901/nemar.nm000182

Examples

>>> from eegdash.dataset import NM000182
>>> dataset = NM000182(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 — NM000182.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 nm000182 to reproduce the tutorial on this dataset.

Citation

Petr Nejedly, Vaclav Kremen, Vladimir Sladky, Jan Cimbalnik, Petr Klimes, … (20). Multicenter iEEG dataset for classification of graphoelements and artifactual signals (MAYO). 10.82901/nemar.nm000182

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000182.

Related & sibling datasets

ON006519IEEG · 21 subj NM000183IEEG · 14 subj ON004127IEEG · 8 subj ON003078IEEG · 6 subj ON005169IEEG · 20 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.10.1

Sidecars

events · channels · electrodes · coordsystem

Provenance

CC0 · 10.82901/nemar.nm000182

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000182`
Title	Multicenter iEEG dataset for classification of graphoelements and artifactual signals (MAYO)
Author (year)	—
Canonical	—
Importable as	`NM000182`
Year	20
Authors	Petr Nejedly, Vaclav Kremen, Vladimir Sladky, Jan Cimbalnik, Petr Klimes, Filip Plesinger, Filip Mivalt, Vojtech Travnicek, Ivo Viscor, Martin Pail, Josef Halamek, Benjamin Brinkmann, Milan Brazdil, Pavel Jurak, Gregory Worrell
License	CC0
Citation / DOI	10.82901/nemar.nm000182
Source links	OpenNeuro \| NeMAR \| Source URL

NM000182: ieeg dataset, 24 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#