EEGdash›NeMAR›NM000481

Iss. 481 · 14 subjects · 14 recordings · CC-BY-4.0

Dataset Brief · s500-literature-201

NM000481: ieeg dataset, 14 subjects#

s500-literature-201: Whole-night hippocampal depth and scalp Cz sleep EEG with spindle and ripple detections, 14 epilepsy patients (Ngo, Fell & Staresina, 2020) (derivative)

Access recordings and metadata through EEGDash.

Citation: Hong-Viet V. Ngo, Juergen Fell, Bernhard P. Staresina (2020). s500-literature-201: Whole-night hippocampal depth and scalp Cz sleep EEG with spindle and ripple detections, 14 epilepsy patients (Ngo, Fell & Staresina, 2020) (derivative). 10.82901/nemar.nm000481

Modality: ieeg Subjects: 14 Recordings: 14 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

14-participant iEEG dataset — s500-literature-201: Whole-night hippocampal depth and scalp Cz sleep EEG with spindle and ripple detections, 14 epilepsy patients (Ngo, Fell & Staresina, 2020) (derivative).

Data & curation Hong-Viet V. Ngo · Juergen Fell · Bernhard P. Staresina
Senior author Bernhard P. Staresina
Year 2020 · Distributed via NeMAR
Funding ERC Starting Grant 'RECALL' (Staresina, as acknowledged in the article) · Deutsche Forschungsgemeinschaft SFB1089 (Fell)

iEEG · 2 ch1000 HzBIDS 1.10.0Task · sleep

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 NM000481

dataset = NM000481(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 = NM000481(cache_dir="./data", subject="01")

Advanced query

dataset = NM000481(
    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{nm000481,
  title = {s500-literature-201: Whole-night hippocampal depth and scalp Cz sleep EEG with spindle and ripple detections, 14 epilepsy patients (Ngo, Fell & Staresina, 2020) (derivative)},
  author = {Hong-Viet V. Ngo and Juergen Fell and Bernhard P. Staresina},
  doi = {10.82901/nemar.nm000481},
  url = {https://doi.org/10.82901/nemar.nm000481},
}

§ 02Study · The README

About This Dataset#

Whole-night sleep recordings from 14 pre-surgical epilepsy patients (Department of Epileptology, University of Bonn),

135.6 h in total, two channels per patient at 1 kHz: HIPP, a posterior hippocampal depth contact of the non-pathological hemisphere (the contact with the highest spindle density, article), and NC, scalp electrode Cz. Released by the authors on OSF (https://osf.io/3hpvr/, CC-BY 4.0) with their ripple and spindle detections, sleep scoring and artifact intervals. THIS IS A DERIVATIVE DATASET: the release holds the two channels the authors analysed (referenced to linked mastoids, hardware band-pass 0.01-300 Hz), not the full clinical recordings; the other scalp electrodes (C3, C4, Oz), EOG and EMG are not released.

released float64 values (unit µV assumed from the article’s thresholds; not stated in the release).

DOI

Ngo, Fell & Staresina (2020): whole-night hippocampal and scalp sleep EEG with spindle and ripple detections

events.tsv: (1) sleep stages as runs of the released per-sample scoring (sleep_stage_wake/S1/S2/S3/S4/REM; the release codes 0-5 are kept in the sleep_stage column; the names follow the Rechtschaffen & Kales scoring reported in the article and the detectors’ NREM stages 2-4); (2) artifact_HIPP / artifact_NC intervals from the authors’ automated artifact detection; (3) ripple_HIPP, spindle_HIPP, spindle_NC events from the authors’ detectors with their stage, rejected flag and envelope amplitude; the detector parameters are in each events.json (DetectorParameters).

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000481-blue)](https://doi.org/10.82901/nemar.nm000481) # Ngo, Fell & Staresina (2020): whole-night hippocampal and scalp sleep EEG with spindle and ripple detections Whole-night sleep recordings from 14 pre-surgical epilepsy patients (Department of Epileptology, University of Bonn), 135.6 h in total, two channels per patient at 1 kHz: HIPP, a posterior hippocampal depth contact of the non-pathological hemisphere (the contact with the highest spindle density, article), and NC, scalp electrode Cz. Released by the authors on OSF (https://osf.io/3hpvr/, CC-BY 4.0) with their ripple and spindle detections, sleep scoring and artifact intervals. THIS IS A DERIVATIVE DATASET: the release holds the two channels the authors analysed (referenced to linked mastoids, hardware band-pass 0.01-300 Hz), not the full clinical recordings; the other scalp electrodes (C3, C4, Oz), EOG and EMG are not released. ## Contents - sub-NN/ieeg/sub-NN_task-sleep_run-1_ieeg.*: the night’s continuous signal (HIPP typed SEEG, NC typed EEG), float32 of the

released float64 values (unit µV assumed from the article’s thresholds; not stated in the release).

events.tsv: (1) sleep stages as runs of the released per-sample scoring (sleep_stage_wake/S1/S2/S3/S4/REM; the release codes 0-5 are kept in the sleep_stage column; the names follow the Rechtschaffen & Kales scoring reported in the article and the detectors’ NREM stages 2-4); (2) artifact_HIPP / artifact_NC intervals from the authors’ automated artifact detection; (3) ripple_HIPP, spindle_HIPP, spindle_NC events from the authors’ detectors with their stage, rejected flag and envelope amplitude; the detector parameters are in each events.json (DetectorParameters).
electrodes.tsv (space-Other): MNI coordinates of the HIPP contact from Table 3 of the article (written because the release patient numbering matches the article; the template version is not stated). Verification: detected minus rejected spindle counts per patient (release / Table 2):

patient | NC spindles | HIPP spindles | ripples |

|---|—|---|—| | 1 | 1440 / 1440 | 1296 / 1296 | 255 / 135 | | 2 | 1243 / 1243 | 571 / 571 | 410 / 107 | | 3 | 1703 / 1703 | 1725 / 1725 | 333 / 116 | | 4 | 1074 / 1074 | 978 / 978 | 1130 / 626 | | 5 | 1427 / 1427 | 625 / 625 | 910 / 206 | | 6 | 1001 / 1001 | 691 / 691 | 769 / 153 | | 7 | 903 / 903 | 1975 / 1975 | 1869 / 403 | | 8 | 1004 / 1004 | 892 / 892 | 1167 / 400 | | 9 | 2079 / 2079 | 2038 / 2038 | 622 / 518 | | 10 | 1120 / 1120 | 840 / 840 | 412 / 245 | | 11 | 494 / 494 | 739 / 739 | 256 / 208 | | 12 | 448 / 448 | 417 / 417 | 113 / 44 | | 13 | 1339 / 1339 | 406 / 406 | 416 / 45 | | 14 | 1899 / 1899 | 1555 / 1555 | 808 / 542 | - participants.tsv: no per-patient age or sex in the release (group: 35.4 +- 3.0 years, 7 of 14 female). - sourcedata/osf-3hpvr/: the release files (EEGs, Ripples, Spindles, the control-events zip and the figure source data +

scripts) with the MAT text-header creation dates reduced to month/year; DEIDENTIFICATION_MANIFEST.tsv lists sha-256 before and after.

## Privacy The release contains no names, dates or identifiers; patients are numbered pat01-pat14. MAT header dates were reduced to month/year.

License: CC-BY-4.0

Authors:

Hong-Viet V. Ngo
Juergen Fell
Bernhard P. Staresina

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000481

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 2 ch (n=14 recordings)

Sampling frequencies: 1000.0 Hz (n=14 recordings)

Total recording duration: 135 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 2 ch · iEEG · 1000 Hz · 14 subjects, 14 recordings

Live trace viewer — sub-04 · task-sleep · run-1

Showing one representative recording out of 14 subjects and 14 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.

Electrode layout — iEEG · 1 sensors — 1 channels

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.NM000481(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000481

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

class eegdash.dataset.NM000481(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

s500-literature-201: Whole-night hippocampal depth and scalp Cz sleep EEG with spindle and ripple detections, 14 epilepsy patients (Ngo, Fell & Staresina, 2020) (derivative)

Study:: nm000481 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000481.

Modality: ieeg; Subject type: Unknown. Subjects: 14; recordings: 14; 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/nm000481 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000481 DOI: https://doi.org/10.82901/nemar.nm000481

Examples

>>> from eegdash.dataset import NM000481
>>> dataset = NM000481(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 — NM000481.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 nm000481 to reproduce the tutorial on this dataset.

Citation

Hong-Viet V. Ngo, Juergen Fell, Bernhard P. Staresina (2020). s500-literature-201: Whole-night hippocampal depth and scalp Cz sleep EEG with spindle and ripple detections, 14 epilepsy patients (Ngo, Fell & Staresina, 2020) (derivative). 10.82901/nemar.nm000481

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000481.

Related & sibling datasets

NM000473IEEG · 10 subj NM000405IEEG · 16 subj NM000432IEEG · 18 subj NM000480IEEG · 11 subj NM000482IEEG · 7 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.nm000481

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000481`
Title	s500-literature-201: Whole-night hippocampal depth and scalp Cz sleep EEG with spindle and ripple detections, 14 epilepsy patients (Ngo, Fell & Staresina, 2020) (derivative)
Author (year)	—
Canonical	—
Importable as	`NM000481`
Year	2020
Authors	Hong-Viet V. Ngo, Juergen Fell, Bernhard P. Staresina
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000481
Source links	OpenNeuro \| NeMAR \| Source URL

NM000481: ieeg dataset, 14 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#