EEGdash›OpenNeuro›DS008115

Iss. 8115 · 180 subjects · 180 recordings · CC0

Dataset Brief · ValidPain2 - Performance of the Nociception Level Index and t…

DS008115: eeg dataset, 180 subjects#

ValidPain2 - Performance of the Nociception Level Index and the PainSensor to predict and detect responsiveness to nociceptive procedures in critical care patients

Access recordings and metadata through EEGDash.

Citation: Bublitz, Viktor Karl ORCID: 0000-0001-7819-9134; Ringat, Teresa; Jurth, Carlo; Lichtner, Gregor; von Dincklage, Falk (2026). ValidPain2 - Performance of the Nociception Level Index and the PainSensor to predict and detect responsiveness to nociceptive procedures in critical care patients. 10.18112/openneuro.ds008115.v1.0.0

Modality: eeg Subjects: 180 Recordings: 180 License: CC0 Source: openneuro

Metadata: Complete (100%)

180-participant EEG dataset — ValidPain2 - Performance of the Nociception Level Index and the PainSensor to predict and detect responsiveness to nociceptive procedures in critical care patients.

Data & curation Bublitz, Viktor Karl ORCID: 0000-0001-7819-9134; Ringat, Teresa; Jurth, Carlo; Lichtner, Gregor; von Dincklage, Falk
Senior author Bublitz, Viktor Karl ORCID: 0000-0001-7819-9134; Ringat, Teresa; Jurth, Carlo; Lichtner, Gregor; von Dincklage, Falk
Year 2026 · Distributed via OpenNeuro
Funding none

EEG · 4 ch178 HzBIDS 1.9.03 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 DS008115

dataset = DS008115(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 = DS008115(cache_dir="./data", subject="01")

Advanced query

dataset = DS008115(
    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{ds008115,
  title = {ValidPain2 - Performance of the Nociception Level Index and the PainSensor to predict and detect responsiveness to nociceptive procedures in critical care patients},
  author = {Bublitz, Viktor Karl ORCID: 0000-0001-7819-9134; Ringat, Teresa; Jurth, Carlo; Lichtner, Gregor; von Dincklage, Falk},
  doi = {10.18112/openneuro.ds008115.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds008115.v1.0.0},
}

§ 02Study · The README

About This Dataset#

This anonymous dataset belongs to the following open-access publication:

https://doi.org/10.1016/j.jcrc.2025.155090 Title: Performance of the Nociception Level Index and the PainSensor to predict and detect responsiveness to nociceptive procedures in critical care patients Brief description:

Frontal EEG recordings and nociception monitor recordings during putatively

noxious and non-noxious interventions in mechanically-ventilated intesive care patients unable to self-report pain.

The study was approved by the appropriate ethics committee (EA1/151/16) and was registered at the German registry for clinical trials (DRKS00011206; Universal Trial Number: U1111–1189-2772).

Please properly cite the publication when working with the data set! Viktor Bublitz, Teresa Ringat, Carlo Jurth, Gregor Lichtner, Falk von Dincklage, Performance of the Nociception Level Index and the PainSensor to predict and detect responsiveness to nociceptive procedures in critical care patients, Journal of Critical Care, Volume 88, 2025, 155090, ISSN 0883-9441, https://doi.org/10.1016/j.jcrc.2025.155090. (https://www.sciencedirect.com/science/article/pii/S0883944125000772)

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 4 ch (n=180 recordings)

Sampling frequencies: 178.1538462 Hz (n=180 recordings)

Total recording duration: 26 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 4 ch · EEG · 178 Hz · 180 subjects, 180 recordings

Live trace viewer — sub-166 · task-Positioning

Showing one representative recording out of 180 subjects and 180 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 4 sensors — 4 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.DS008115(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asDS008115

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

class eegdash.dataset.DS008115(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

ValidPain2 - Performance of the Nociception Level Index and the PainSensor to predict and detect responsiveness to nociceptive procedures in critical care patients

Study:: ds008115 (OpenNeuro)
Author (year):: —
Canonical:: —

Also importable as: DS008115.

Modality: eeg; Subject type: Unknown. Subjects: 180; recordings: 180; tasks: 3.

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/ds008115 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds008115 DOI: https://doi.org/10.18112/openneuro.ds008115.v1.0.0

Examples

>>> from eegdash.dataset import DS008115
>>> dataset = DS008115(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 — DS008115.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 ds008115 to reproduce the tutorial on this dataset.

Citation

Bublitz, Viktor Karl ORCID: 0000-0001-7819-9134; Ringat, Teresa; Jurth, Carlo; Lichtner, Gregor; von Dincklage, Falk (2026). ValidPain2 - Performance of the Nociception Level Index and the PainSensor to predict and detect responsiveness to nociceptive procedures in critical care patients. 10.18112/openneuro.ds008115.v1.0.0

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds008115.v1.0.0.

Related & sibling datasets

DS007823EEG · 173 subj DS008108EEG · 142 subj DS008092EEG · 77 subj DS008730EEG · 104 subj DS007827EEG · 99 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds008115.v1.0.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`DS008115`
Title	ValidPain2 - Performance of the Nociception Level Index and the PainSensor to predict and detect responsiveness to nociceptive procedures in critical care patients
Author (year)	—
Canonical	—
Importable as	`DS008115`
Year	2026
Authors	Bublitz, Viktor Karl ORCID: 0000-0001-7819-9134; Ringat, Teresa; Jurth, Carlo; Lichtner, Gregor; von Dincklage, Falk
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds008115.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS008115: eeg dataset, 180 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#