EEGdash›NeMAR›NM000424

Iss. 424 · 13 subjects · 24 recordings · CC-BY-4.0

Dataset Brief · Human subthalamic nucleus LFP during a stop-signal task (STN…

NM000424: ieeg dataset, 13 subjects#

Human subthalamic nucleus LFP during a stop-signal task (STN delta; Szabo et al.; derivative, 250 Hz)

Access recordings and metadata through EEGDash.

Citation: Johanna Petra Szabo, Panna Hegedüs, Tamás Laszlovszky, László Halász, Gabriella Miklós, Bálint Király, György Perczel, Virág Bokodi, László Entz, István Ulbert, Gertrúd Tamás, Dániel Fabó, Loránd Erőss, Balázs Hangya (2026). Human subthalamic nucleus LFP during a stop-signal task (STN delta; Szabo et al.; derivative, 250 Hz). 10.82901/nemar.nm000424

Modality: ieeg Subjects: 13 Recordings: 24 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

13-participant iEEG dataset — Human subthalamic nucleus LFP during a stop-signal task (STN delta; Szabo et al.; derivative, 250 Hz).

Data & curation Johanna Petra Szabo · Panna Hegedüs · Tamás Laszlovszky · László Halász · Gabriella Miklós · Bálint Király · …
Senior author Balázs Hangya
Year 2026 · Distributed via NeMAR

iEEG · 5 (18), 3 (5), 4 ch250 HzBIDS 1.10.0Task · stopsignal

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 NM000424

dataset = NM000424(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 = NM000424(cache_dir="./data", subject="01")

Advanced query

dataset = NM000424(
    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{nm000424,
  title = {Human subthalamic nucleus LFP during a stop-signal task (STN delta; Szabo et al.; derivative, 250 Hz)},
  author = {Johanna Petra Szabo and Panna Hegedüs and Tamás Laszlovszky and László Halász and Gabriella Miklós and Bálint Király and György Perczel and Virág Bokodi and László Entz and István Ulbert and Gertrúd Tamás and Dániel Fabó and Loránd Erőss and Balázs Hangya},
  doi = {10.82901/nemar.nm000424},
  url = {https://doi.org/10.82901/nemar.nm000424},
}

§ 02Study · The README

About This Dataset#

Intraoperative local field potentials from the subthalamic nucleus (STN) of patients with Parkinson’s disease

undergoing DBS surgery, recorded with microelectrodes while they performed a stop-signal reaction time task. THIS IS A DERIVATIVE DATASET. The release provides the continuous STN LFP only as the authors’ filtered 250 Hz EEGLAB files. Each recording exists in a 0.5 Hz and a 2 Hz high-pass version; only the 0.5 Hz version is included here.

Both Figshare files matched their MD5 checksums.

DOI

Human STN LFP during a stop-signal task (STN delta) - DERIVATIVE

The original release (human-STN-delta_data.zip, 8.5 GB) also contains single-/multi-unit waveforms and spike times (MClust/CellBase), behavioural trial files, scalp EEG (intraoperative and postoperative) and epoched/CSD files. They are not repackaged here: BIDS-iEEG has no spike format, scalp EEG is not intracranial, and the epoched files duplicate the continuous data. Get them from the Figshare record. The release’s session folder names are surgery dates, so they are not copied.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000424-blue)](https://doi.org/10.82901/nemar.nm000424) # Human STN LFP during a stop-signal task (STN delta) - DERIVATIVE Intraoperative local field potentials from the subthalamic nucleus (STN) of patients with Parkinson’s disease undergoing DBS surgery, recorded with microelectrodes while they performed a stop-signal reaction time task. THIS IS A DERIVATIVE DATASET. The release provides the continuous STN LFP only as the authors’ filtered 250 Hz EEGLAB files. Each recording exists in a 0.5 Hz and a 2 Hz high-pass version; only the 0.5 Hz version is included here. ## Source - Figshare: Johanna Petra Szabo, Panna Hegedüs, Tamás Laszlovszky, László Halász, Gabriella Miklós, Bálint Király, György Perczel, Virág Bokodi, László Entz, István Ulbert, Gertrúd Tamás, Dániel Fabó, Loránd Erőss, Balázs Hangya. “Dataset for “Neurons of the human subthalamic nucleus engage with local delta frequency processes during action cancellation””. doi:10.6084/m9.figshare.31359616 (CC BY 4.0).

Both Figshare files matched their MD5 checksums.

The original release (human-STN-delta_data.zip, 8.5 GB) also contains single-/multi-unit waveforms and spike times (MClust/CellBase), behavioural trial files, scalp EEG (intraoperative and postoperative) and epoched/CSD files. They are not repackaged here: BIDS-iEEG has no spike format, scalp EEG is not intracranial, and the epoched files duplicate the continuous data. Get them from the Figshare record. The release’s session folder names are surgery dates, so they are not copied.

## Contents - 13 patients (sub-pd<NN> = release patient id), 24 recordings, 5.13 h at 250 Hz. - ses-intraop<k>: intraoperative session k of that patient (ordered by date; dates removed). - acq-<side>stim<off|on>: hemisphere and the release’s stimulation label (folder stim<off|on>_<side>_LFP). - Channels Ch1..Ch5: the release’s microelectrode tracks (type DBS, µV, values unchanged as float32). - events.tsv: the EEGLAB annotations (StimulusOn, KeyPress1, Feedback, StopSignal, …). - No coordinates are released; electrodes.tsv lists the tracks with x, y, z = n/a and the stated target (STN). ## Privacy Recording dates (in the release folder names) are dropped. No names were found. ## Ethics Verbatim from the article of this Figshare dataset, Szabo JP et al. (2026) “Neurons of the human subthalamic nucleus engage with local delta frequency processes during action cancellation”, Nat Commun, doi:10.1038/s41467-026-71502-z (Europe PMC PMC13287684, Methods, Patient selection): > “Thirteen patients with Parkinson-disease (6 males; age, 60.92 ± 12.6 years; Supplementary Table S1) undergoing bilateral STN > DBS implantation (Supplementary Table S2) were enrolled in this study, recruited from the Institute of Neurosurgery and > Neurointervention (formerly National Institute of Clinical Neuroscience) of Semmelweis University (Budapest, Hungary). Informed > consent was obtained from all subjects. Informed consent forms and all research protocols were approved by the Institutional > Research Ethics Committee of NIMNN or its predecessors (OKITI IKEB1/2018; OMIII IKEB 21/2023) and the National Scientific and > Ethical Committee of the Medical Research Council, Budapest, Hungary (OGYÉI/64814-6/2023).” The 13 patients of the article are the 13 patients of this dataset.

License: CC-BY-4.0

Authors:

Johanna Petra Szabo
Panna Hegedüs
Tamás Laszlovszky
László Halász
Gabriella Miklós
… and 9 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000424

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts (ch)

345

Sampling frequencies: 250.0 Hz (n=24 recordings)

Total recording duration: 5 h 7 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 5 (18), 3 (5), 4 ch · iEEG · 250 Hz · 13 subjects, 24 recordings

Live trace viewer — sub-pd14 · ses-intraop1 · task-stopsignal · run-1

Showing one representative recording out of 13 subjects and 24 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.NM000424(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000424

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

class eegdash.dataset.NM000424(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Human subthalamic nucleus LFP during a stop-signal task (STN delta; Szabo et al.; derivative, 250 Hz)

Study:: nm000424 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000424.

Modality: ieeg; Subject type: Unknown. Subjects: 13; recordings: 24; 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/nm000424 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000424 DOI: https://doi.org/10.82901/nemar.nm000424

Examples

>>> from eegdash.dataset import NM000424
>>> dataset = NM000424(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 — NM000424.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 nm000424 to reproduce the tutorial on this dataset.

Citation

Johanna Petra Szabo, Panna Hegedüs, Tamás Laszlovszky, László Halász, Gabriella Miklós, … (2026). Human subthalamic nucleus LFP during a stop-signal task (STN delta; Szabo et al.; derivative, 250 Hz). 10.82901/nemar.nm000424

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000424.

Related & sibling datasets

NM000471IEEG · 11 subj NM000476IEEG · 5 subj NM000409IEEG · 8 subj NM000400IEEG · 7 subj NM000462IEEG · 3 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.nm000424

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000424`
Title	Human subthalamic nucleus LFP during a stop-signal task (STN delta; Szabo et al.; derivative, 250 Hz)
Author (year)	—
Canonical	—
Importable as	`NM000424`
Year	2026
Authors	Johanna Petra Szabo, Panna Hegedüs, Tamás Laszlovszky, László Halász, Gabriella Miklós, Bálint Király, György Perczel, Virág Bokodi, László Entz, István Ulbert, Gertrúd Tamás, Dániel Fabó, Loránd Erőss, Balázs Hangya
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000424
Source links	OpenNeuro \| NeMAR \| Source URL

NM000424: ieeg dataset, 13 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#