EEGdash›NeMAR›NM000394

Iss. 394 · 1 subjects · 34 recordings · CC-BY-NC-SA-4.0

Dataset Brief · Intracranial current source density (CSD) during epileptic se…

NM000394: ieeg dataset, 1 subjects#

Intracranial current source density (CSD) during epileptic seizures and interictal periods, one patient (Budapest)

Access recordings and metadata through EEGDash.

Citation: Dániel Fabó, Loránd Erőss, Zsigmond Benkő (20). Intracranial current source density (CSD) during epileptic seizures and interictal periods, one patient (Budapest). 10.82901/nemar.nm000394

Modality: ieeg Subjects: 1 Recordings: 34 License: CC-BY-NC-SA-4.0 Source: nemar

Metadata: Complete (100%)

1-participant iEEG dataset — Intracranial current source density (CSD) during epileptic seizures and interictal periods, one patient (Budapest).

Data & curation Dániel Fabó · Loránd Erőss · Zsigmond Benkő
Senior author Zsigmond Benkő
Year 20 · Distributed via NeMAR
Funding Hungarian National Research, Development and Innovation Fund NKFIH K113147 · NKFIH K135837 · + 1 more

iEEG · 4 ch1024 HzBIDS 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 NM000394

dataset = NM000394(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 = NM000394(cache_dir="./data", subject="01")

Advanced query

dataset = NM000394(
    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{nm000394,
  title = {Intracranial current source density (CSD) during epileptic seizures and interictal periods, one patient (Budapest)},
  author = {Dániel Fabó and Loránd Erőss and Zsigmond Benkő},
  doi = {10.82901/nemar.nm000394},
  url = {https://doi.org/10.82901/nemar.nm000394},
}

§ 02Study · The README

About This Dataset#

This dataset contains intracranial Current Source Density (CSD) recordings of 18 epileptic seizures and 16 interictal

segments from an epileptic patient (text of the source README). This is a derivative dataset. The signals are current source density traces computed by the data authors from subdural ECoG; they are not raw electrode voltages. No raw ECoG is part of the source release.

A 20-year-old patient with drug-resistant epilepsy underwent subdural grid and strip implantation (ADTECH, 10 mm

inter-contact spacing) for pre-surgical evaluation. Video-EEG was recorded with a Micromed Brain-Quick System Evolution, referenced to the skull or mastoid, at 1024 Hz. CSD was computed at fronto-lateral (Fl1, Fl2), inferior-parietal (iP) and fronto-basal (Fb) sites. Patients consented to clinical investigation and surgery along institutional review board guidelines, in accordance with the Declaration of Helsinki (paper methods).

DOI

Intracranial current source density during epileptic seizures (Budapest)

Layout

sub-01/ieeg/*_task-seizure_run-XX_desc-csd_ieeg.vhdr: 18 seizure segments (source folder data/seizure)
sub-01/ieeg/*_task-interictal_run-XX_desc-csd_ieeg.vhdr: 16 interictal segments (source folder data/control)
each segment: 20480 samples x 4 channels (GrB6, GrE2, GrF4, FbB3); values copied unchanged from the CSV files as float32
sub-01/sub-01_scans.tsv: maps each run to its original CSV file

View full README

DOI

Intracranial current source density during epileptic seizures (Budapest)

Layout

sub-01/ieeg/*_task-seizure_run-XX_desc-csd_ieeg.vhdr: 18 seizure segments (source folder data/seizure)
sub-01/ieeg/*_task-interictal_run-XX_desc-csd_ieeg.vhdr: 16 interictal segments (source folder data/control)
each segment: 20480 samples x 4 channels (GrB6, GrE2, GrF4, FbB3); values copied unchanged from the CSV files as float32
sub-01/sub-01_scans.tsv: maps each run to its original CSV file
sourcedata/gin-intracranial_csd/: the complete original repository content (README, LICENSE, CSV files), unchanged

Not stated by the source (left as n/a)

Units of the CSD values; onset times of the segments; meaning of the numbers in the CSV file names; mapping of the contact names (GrB6, GrE2, GrF4, FbB3) to the paper’s region labels; sex of the patient; filters.

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. Processing / reference. The deposited signals are CSD, not raw potentials: “CSD was computed (at Fl1, Fl2, iP and Fb), 1–30 Hz Fourier filtering (4th order Butterworth filter) and subsequent rank normalization was carried out” (App. B.2, p.28). The deposited CSV values are integers, so whether the stored files are before or after the filtering and rank normalisation is not stated. Electrode types. AD-TECH subdural strips and a grid with 10 mm inter-contact spacing were implanted through a craniotomy, guided by neuronavigation and fluoroscopy (App. B.2). The text describes them as “a subdural grid and two strip electrodes” (p.11). Localisation. Electrodes were identified on the post-implant CT with BioImage Suite, mapped to the pre-implant MRI with FSL FLIRT/BET2, and projected onto the FreeSurfer pial surface (Dykstra et al. 2012). Intraoperative photographs and electrical stimulation mapping (ESM) were used to corroborate the result (App. B.2). The four analysed sites are fronto-basal (Fb), frontal (Fl1), fronto-lateral (Fl2) and infero-parietal (iP), shown on the patient’s brain surface in Fig. 5F. Neither coordinates nor the patient MRI are published. Most of the high-frequency seizure activity was in Fb and Fl1. The frontal and fronto-basal regions were resected, and the patient was seizure-free for 1 year before a relapse (p.11).

Regions per participant (as published; no coordinates exist)

| participant | region (as stated) | hemisphere | contacts | source |
|---|---|---|---|---|
| sub-01 | fronto-basal (Fb) | n/a | 1 (CSD derivation) | arXiv:1808.10806v4, p.11 Results; Fig. 5A/F; App. B.2 |
| sub-01 | frontal (Fl1) | n/a | 1 (CSD derivation) | arXiv:1808.10806v4, p.11 Results; Fig. 5A/F; App. B.2 |
| sub-01 | fronto-lateral (Fl2) | n/a | 1 (CSD derivation) | arXiv:1808.10806v4, p.11 Results; Fig. 5A/F; App. B.2 |
| sub-01 | infero-parietal (iP) | n/a | 1 (CSD derivation) | arXiv:1808.10806v4, p.11 Results; Fig. 5A/F; App. B.2 |

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000394-blue)](https://doi.org/10.82901/nemar.nm000394) # Intracranial current source density during epileptic seizures (Budapest) This dataset contains intracranial Current Source Density (CSD) recordings of 18 epileptic seizures and 16 interictal segments from an epileptic patient (text of the source README). This is a derivative dataset. The signals are current source density traces computed by the data authors from subdural ECoG; they are not raw electrode voltages. No raw ECoG is part of the source release. ## Source - G-Node GIN: https://gin.g-node.org/zsigmondbenko/intracranial_csd (commit 3e5bd99b6919d3fa33e8c4d8d50c714f131dc20b, 2018-08-31) - Copyright (c) 2018 Dániel Fabó and Loránd Erőss, National Institute for Clinical Neurosciences, “Juhász Pál” Epilepsy Center, Budapest, Hungary - Licence: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 (LICENSE copied verbatim) - Paper that uses these data: Benkő Z. et al., “Complete Inference of Causal Relations between Dynamical Systems”, arXiv:1808.10806 (data availability statement points to this repository). ## Recording (from the paper’s methods) A 20-year-old patient with drug-resistant epilepsy underwent subdural grid and strip implantation (ADTECH, 10 mm inter-contact spacing) for pre-surgical evaluation. Video-EEG was recorded with a Micromed Brain-Quick System Evolution, referenced to the skull or mastoid, at 1024 Hz. CSD was computed at fronto-lateral (Fl1, Fl2), inferior-parietal (iP) and fronto-basal (Fb) sites. Patients consented to clinical investigation and surgery along institutional review board guidelines, in accordance with the Declaration of Helsinki (paper methods). ## Layout - sub-01/ieeg/*_task-seizure_run-XX_desc-csd_ieeg.vhdr: 18 seizure segments (source folder data/seizure) - sub-01/ieeg/*_task-interictal_run-XX_desc-csd_ieeg.vhdr: 16 interictal segments (source folder data/control) - each segment: 20480 samples x 4 channels (GrB6, GrE2, GrF4, FbB3); values copied unchanged from the CSV files as float32 - sub-01/sub-01_scans.tsv: maps each run to its original CSV file - sourcedata/gin-intracranial_csd/: the complete original repository content (README, LICENSE, CSV files), unchanged ## Not stated by the source (left as n/a) Units of the CSD values; onset times of the segments; meaning of the numbers in the CSV file names; mapping of the contact names (GrB6, GrE2, GrF4, FbB3) to the paper’s region labels; sex of the patient; filters. ## 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. Processing / reference. The deposited signals are CSD, not raw potentials: “CSD was computed (at Fl1, Fl2, iP and Fb), 1–30 Hz Fourier filtering (4th order Butterworth filter) and subsequent rank normalization was carried out” (App. B.2, p.28). The deposited CSV values are integers, so whether the stored files are before or after the filtering and rank normalisation is not stated. Electrode types. AD-TECH subdural strips and a grid with 10 mm inter-contact spacing were implanted through a craniotomy, guided by neuronavigation and fluoroscopy (App. B.2). The text describes them as “a subdural grid and two strip electrodes” (p.11). Localisation. Electrodes were identified on the post-implant CT with BioImage Suite, mapped to the pre-implant MRI with FSL FLIRT/BET2, and projected onto the FreeSurfer pial surface (Dykstra et al. 2012). Intraoperative photographs and electrical stimulation mapping (ESM) were used to corroborate the result (App. B.2). The four analysed sites are fronto-basal (Fb), frontal (Fl1), fronto-lateral (Fl2) and infero-parietal (iP), shown on the patient’s brain surface in Fig. 5F. Neither coordinates nor the patient MRI are published. Most of the high-frequency seizure activity was in Fb and Fl1. The frontal and fronto-basal regions were resected, and the patient was seizure-free for 1 year before a relapse (p.11). ### Regions per participant (as published; no coordinates exist) | participant | region (as stated) | hemisphere | contacts | source | |---|—|---|—|---| | sub-01 | fronto-basal (Fb) | n/a | 1 (CSD derivation) | arXiv:1808.10806v4, p.11 Results; Fig. 5A/F; App. B.2 | | sub-01 | frontal (Fl1) | n/a | 1 (CSD derivation) | arXiv:1808.10806v4, p.11 Results; Fig. 5A/F; App. B.2 | | sub-01 | fronto-lateral (Fl2) | n/a | 1 (CSD derivation) | arXiv:1808.10806v4, p.11 Results; Fig. 5A/F; App. B.2 | | sub-01 | infero-parietal (iP) | n/a | 1 (CSD derivation) | arXiv:1808.10806v4, p.11 Results; Fig. 5A/F; App. B.2 |

License: CC-BY-NC-SA-4.0

Authors:

Dániel Fabó
Loránd Erőss
Zsigmond Benkő

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000394

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=1, range 20–20 yr, mean 20.0 yr)

20

Other · 1

Channel counts: 4 ch (n=34 recordings)

Sampling frequencies: 1024.0 Hz (n=34 recordings)

Total recording duration: 11 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 4 ch · iEEG · 1024 Hz · 1 subjects, 34 recordings

Live trace viewer — sub-01 · task-seizure · run-17

Showing one representative recording out of 1 subjects and 34 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.NM000394(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000394

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

class eegdash.dataset.NM000394(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Intracranial current source density (CSD) during epileptic seizures and interictal periods, one patient (Budapest)

Study:: nm000394 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000394.

Modality: ieeg; Subject type: Unknown. Subjects: 1; recordings: 34; 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/nm000394 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000394 DOI: https://doi.org/10.82901/nemar.nm000394

Examples

>>> from eegdash.dataset import NM000394
>>> dataset = NM000394(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 — NM000394.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 nm000394 to reproduce the tutorial on this dataset.

Citation

Dániel Fabó, Loránd Erőss, Zsigmond Benkő (20). Intracranial current source density (CSD) during epileptic seizures and interictal periods, one patient (Budapest). 10.82901/nemar.nm000394

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000394.

Related & sibling datasets

NM000383IEEG · 6 subj NM000350IEEG · 6 subj NM000399IEEG · 11 subj NM000387IEEG · 9 subj NM000386IEEG · 6 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.10.0

Sidecars

channels · eeg.json

Provenance

CC-BY-NC-SA-4.0 · 10.82901/nemar.nm000394

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000394`
Title	Intracranial current source density (CSD) during epileptic seizures and interictal periods, one patient (Budapest)
Author (year)	—
Canonical	—
Importable as	`NM000394`
Year	20
Authors	Dániel Fabó, Loránd Erőss, Zsigmond Benkő
License	CC-BY-NC-SA-4.0
Citation / DOI	10.82901/nemar.nm000394
Source links	OpenNeuro \| NeMAR \| Source URL

NM000394: 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#