EEGdash›NeMAR›NM000513

Iss. 513 · 7 subjects · 9 recordings · CC-BY-4.0

Dataset Brief · Simultaneous EEG-SEEG recordings of deep mesial temporal sour…

NM000513: ieeg dataset, 7 subjects#

Simultaneous EEG-SEEG recordings of deep mesial temporal sources (interictal spike epochs)

Access recordings and metadata through EEGDash.

Citation: Laurent Koessler, Eric Ternisien, Thierry Cecchin, Sophie Colnat-Coulbois, Louis Maillard (19). Simultaneous EEG-SEEG recordings of deep mesial temporal sources (interictal spike epochs). 10.82901/nemar.nm000513

Modality: ieeg Subjects: 7 Recordings: 9 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

7-participant iEEG dataset — Simultaneous EEG-SEEG recordings of deep mesial temporal sources (interictal spike epochs).

Data & curation Laurent Koessler · Eric Ternisien · Thierry Cecchin · Sophie Colnat-Coulbois · Louis Maillard
Senior author Louis Maillard
Year 19 · Distributed via NeMAR

iEEG · 86 (2), 114 (2), 102 (2), 88, 96, 77 ch512 HzBIDS 1.10.0Task · iis

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 NM000513

dataset = NM000513(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 = NM000513(cache_dir="./data", subject="01")

Advanced query

dataset = NM000513(
    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{nm000513,
  title = {Simultaneous EEG-SEEG recordings of deep mesial temporal sources (interictal spike epochs)},
  author = {Laurent Koessler and Eric Ternisien and Thierry Cecchin and Sophie Colnat-Coulbois and Louis Maillard},
  doi = {10.82901/nemar.nm000513},
  url = {https://doi.org/10.82901/nemar.nm000513},
}

§ 02Study · The README

About This Dataset#

Koessler L, Ternisien E, Cecchin T, Colnat-Coulbois S, Maillard L (Université de Lorraine / CHRU Nancy), Mendeley Data,

doi:10.17632/8wz3wvm9y5, CC BY 4.0. Release description (abridged): nine simultaneous EEG-SEEG datasets corresponding to mesial temporal networks in seven patients (three females, mean age 38 years) with temporal lobe epilepsy. Within each network the earliest interictal intracerebral spikes (IIS) with the highest amplitude were marked at the peak of the initial component (t0); EEG and SEEG segments are 1 s epochs centred on t0, band-pass filtered 1.5-30 Hz, no artifact rejection. Acquisition: 128-channel system, 512 Hz, analog 0.15 Hz high-pass, scalp FPz reference. Approved by the local ethics committee (CHRU Nancy).

simultaneous scalp EEG channels (release labels prefixed s, typed EEG), µV, float32, epochs written back to back

(New Segment marker per epoch); events.tsv gives one row per epoch with t0 at 0.5 s.

DOI

Simultaneous EEG-SEEG recordings of deep mesial temporal sources (Mendeley Data 8wz3wvm9y5)

Units: µV, from the release description (amplitudes and thresholds are given in µV); the .mat files do not store units.
Each release cell array has one extra last row with a different length (775 samples) that is not a 512-sample epoch; it is not written to the BIDS runs (kept in sourcedata; see code/conversion_report.json, dropped_rows).
No contact coordinates are released: electrodes.tsv lists the contacts implied by the bipolar labels with n/a coordinates. Shaft -> structure table from the release (SEEG electrodes.docx, internal/external structure): P temporal pole/temporal pole, A amygdala/superior temporal gyrus, B anterior hippocampus/middle temporal gyrus, C posterior hippocampus/middle temporal gyrus, TB entorhinal cortex/inferior temporal gyrus, TM parahippocampal gyrus/middle temporal gyrus, F fusiform gyrus/middle temporal gyrus, H insular cortex/Heschl gyrus, T insular cortex/superior temporal gyrus. Shafts with a b suffix (Ab, Bb, …) are not described in the release.
Age and sex per patient are not released.

sourcedata/mendeley-8wz3wvm9y5/

Release .mat files and the electrode table; MAT text-header creation day set to 01 (DEIDENTIFICATION_MANIFEST.tsv).

References

Koessler L et al. Catching the invisible: mesial temporal source contribution to simultaneous EEG and SEEG recordings. Brain Topogr 2015;28:5-20. doi:10.1007/s10548-014-0417-z
Ternisien E et al. Extracting the invisible: mesial temporal source detection in simultaneous EEG and SEEG recordings. doi:10.21203/rs.3.rs-2201044/v1

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000513-blue)](https://doi.org/10.82901/nemar.nm000513) # Simultaneous EEG-SEEG recordings of deep mesial temporal sources (Mendeley Data 8wz3wvm9y5) Koessler L, Ternisien E, Cecchin T, Colnat-Coulbois S, Maillard L (Université de Lorraine / CHRU Nancy), Mendeley Data, doi:10.17632/8wz3wvm9y5, CC BY 4.0. Release description (abridged): nine simultaneous EEG-SEEG datasets corresponding to mesial temporal networks in seven patients (three females, mean age 38 years) with temporal lobe epilepsy. Within each network the earliest interictal intracerebral spikes (IIS) with the highest amplitude were marked at the peak of the initial component (t0); EEG and SEEG segments are 1 s epochs centred on t0, band-pass filtered 1.5-30 Hz, no artifact rejection. Acquisition: 128-channel system, 512 Hz, analog 0.15 Hz high-pass, scalp FPz reference. Approved by the local ethics committee (CHRU Nancy). ## Content (7 participants, 9 runs, 1945 spike epochs) - sub-<p> = patient P<p>; acq-net<n> = mesial network N<n> (patients 1 and 3 have two networks). - One epoched BrainVision file per network: the bipolar SEEG channels (labels <shaft><i>_<j>, typed SEEG) followed by the

simultaneous scalp EEG channels (release labels prefixed s, typed EEG), µV, float32, epochs written back to back (New Segment marker per epoch); events.tsv gives one row per epoch with t0 at 0.5 s.

Units: µV, from the release description (amplitudes and thresholds are given in µV); the .mat files do not store units.
Each release cell array has one extra last row with a different length (775 samples) that is not a 512-sample epoch; it is not written to the BIDS runs (kept in sourcedata; see code/conversion_report.json, dropped_rows).
No contact coordinates are released: electrodes.tsv lists the contacts implied by the bipolar labels with n/a coordinates. Shaft -> structure table from the release (SEEG electrodes.docx, internal/external structure): P temporal pole/temporal pole, A amygdala/superior temporal gyrus, B anterior hippocampus/middle temporal gyrus, C posterior hippocampus/middle temporal gyrus, TB entorhinal cortex/inferior temporal gyrus, TM parahippocampal gyrus/middle temporal gyrus, F fusiform gyrus/middle temporal gyrus, H insular cortex/Heschl gyrus, T insular cortex/superior temporal gyrus. Shafts with a b suffix (Ab, Bb, …) are not described in the release.
Age and sex per patient are not released.

## sourcedata/mendeley-8wz3wvm9y5/ Release .mat files and the electrode table; MAT text-header creation day set to 01 (DEIDENTIFICATION_MANIFEST.tsv). ## References - Koessler L et al. Catching the invisible: mesial temporal source contribution to simultaneous EEG and SEEG recordings. Brain Topogr 2015;28:5-20. doi:10.1007/s10548-014-0417-z - Ternisien E et al. Extracting the invisible: mesial temporal source detection in simultaneous EEG and SEEG recordings. doi:10.21203/rs.3.rs-2201044/v1

License: CC-BY-4.0

Authors:

Laurent Koessler
Eric Ternisien
Thierry Cecchin
Sophie Colnat-Coulbois
Louis Maillard

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000513

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts (ch)

77868896102114

Sampling frequencies: 512.0 Hz (n=9 recordings)

Total recording duration: 32 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 86 (2), 114 (2), 102 (2), 88, 96, 77 ch · iEEG · 512 Hz · 7 subjects, 9 recordings

Live trace viewer — sub-04 · task-iis

Showing one representative recording out of 7 subjects and 9 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.NM000513(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000513

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

class eegdash.dataset.NM000513(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Simultaneous EEG-SEEG recordings of deep mesial temporal sources (interictal spike epochs)

Study:: nm000513 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000513.

Modality: ieeg; Subject type: Unknown. Subjects: 7; recordings: 9; 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/nm000513 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000513 DOI: https://doi.org/10.82901/nemar.nm000513

Examples

>>> from eegdash.dataset import NM000513
>>> dataset = NM000513(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 — NM000513.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 nm000513 to reproduce the tutorial on this dataset.

Citation

Laurent Koessler, Eric Ternisien, Thierry Cecchin, Sophie Colnat-Coulbois, Louis Maillard (19). Simultaneous EEG-SEEG recordings of deep mesial temporal sources (interictal spike epochs). 10.82901/nemar.nm000513

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000513.

Related & sibling datasets

NM000409IEEG · 8 subj NM000501IEEG · 4 subj NM000400IEEG · 7 subj NM000476IEEG · 5 subj NM000472IEEG · 1 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.10.0

Sidecars

events · channels · eeg.json

Provenance

CC-BY-4.0 · 10.82901/nemar.nm000513

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000513`
Title	Simultaneous EEG-SEEG recordings of deep mesial temporal sources (interictal spike epochs)
Author (year)	—
Canonical	—
Importable as	`NM000513`
Year	19
Authors	Laurent Koessler, Eric Ternisien, Thierry Cecchin, Sophie Colnat-Coulbois, Louis Maillard
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000513
Source links	OpenNeuro \| NeMAR \| Source URL

NM000513: ieeg dataset, 7 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#