EEGdash›NeMAR›ON004621

Iss. 4621 · 42 subjects · 167 recordings · CC0

Dataset Brief · The Nencki-Symfonia EEG/ERP dataset

ON004621: eeg dataset, 42 subjects#

The Nencki-Symfonia EEG/ERP dataset

Access recordings and metadata through EEGDash.

Citation: Dzianok Patrycja, Antonova Ingrida, Wojciechowski Jakub, Dreszer Joanna, Kublik Ewa (20). The Nencki-Symfonia EEG/ERP dataset. 10.82901/nemar.on004621

Modality: eeg Subjects: 42 Recordings: 167 License: CC0 Source: nemar

Metadata: Complete (100%)

42-participant EEG dataset — The Nencki-Symfonia EEG/ERP dataset.

Data & curation Dzianok Patrycja · Antonova Ingrida · Wojciechowski Jakub · Dreszer Joanna · Kublik Ewa
Senior author Kublik Ewa
Year 20 · Distributed via NeMAR
Funding Polish National Science Center (NCN) grant no. 2016/20/W/NZ4/00354

EEG · 127 ch1000 HzBIDS 1.0.24 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 ON004621

dataset = ON004621(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 = ON004621(cache_dir="./data", subject="01")

Advanced query

dataset = ON004621(
    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{on004621,
  title = {The Nencki-Symfonia EEG/ERP dataset},
  author = {Dzianok Patrycja and Antonova Ingrida and Wojciechowski Jakub and Dreszer Joanna and Kublik Ewa},
  doi = {10.82901/nemar.on004621},
  url = {https://doi.org/10.82901/nemar.on004621},
}

§ 02Study · The README

About This Dataset#

The Nencki-Symfonia EEG/ERP dataset (dataset DOI: doi.org/10.5524/100990)

IMPORTANT NOTE: The dataset contains no errors (BIDS-1). The numerous warnings currently displayed are a result of OpenNeuro updating its validator to BIDS-2. The OpenNeuro team is actively working on refining the validator to display only meaningful warnings (more information on OpenNeuro GitHub page). At this time, as dataset owners, we are unable to take any action to resolve these warnings.

Description: mixed cognitive tasks [(i) an extended multi-source interference task, MSIT+; (ii) a 3-stimuli oddball task; (iii) a control, simple reaction task, SRT; and (iv) a resting-state protocol]

Please cite the following references if you use these data: 1. Dzianok P, Antonova I, Wojciechowski J, Dreszer J, Kublik E. The Nencki-Symfonia electroencephalography/event-related potential dataset: Multiple cognitive tasks and resting-state data collected in a sample of healthy adults. Gigascience. 2022 Mar 7;11:giac015. doi: 10.1093/gigascience/giac015. 2. Dzianok P, Antonova I, Wojciechowski J, Dreszer J, Kublik E. Supporting data for “The Nencki-Symfonia EEG/ERP dataset: Multiple cognitive tasks and resting-state data collected in a sample of healthy adults” GigaScience Database, 2022. http://doi.org/10.5524/100990

DOI

Release history: 26/01/2022: Initial release (GigaDB) 15/06/2023: Added to OpenNeuro; updated README and dataset_description.json; minor updated to .json files related with BIDS errors/warnings. Updated events files (ms changed to s). 12/10/2023: public release on OpenNeuro after deleting some additional, not needed system information from raw logfiles 10/2024: minor correction of logfiles in the /sourcedata directory (MSIT and SRT) for sub-01 to sub-03 02/2025 (v1.0.3): corrections to REST files for subjects sub-20 and sub-23 (EEG and .tsv files) – corrected marker names and removed redundant markers

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=42, range 20–34 yr, mean 24.6 yr)

202530

Female · 22Male · 20

Sex composition

Female
22
Male
20
F : M ratio
1.10 : 1

52% female · n = 42 subjects with reported sex.

Channel counts: 127 ch (n=167 recordings)

Sampling frequencies: 1000.0 Hz (n=167 recordings)

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 127 ch · EEG · 1000 Hz · 42 subjects, 167 recordings

Live trace viewer — sub-17 · task-srt

Showing one representative recording out of 42 subjects and 167 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.

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.ON004621(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asON004621

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

class eegdash.dataset.ON004621(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

The Nencki-Symfonia EEG/ERP dataset

Study:: on004621 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: ON004621.

Modality: eeg; Subject type: Unknown. Subjects: 42; recordings: 167; tasks: 4.

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/on004621 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on004621 DOI: https://doi.org/10.82901/nemar.on004621

Examples

>>> from eegdash.dataset import ON004621
>>> dataset = ON004621(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 — ON004621.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 on004621 to reproduce the tutorial on this dataset.

Citation

Dzianok Patrycja, Antonova Ingrida, Wojciechowski Jakub, Dreszer Joanna, Kublik Ewa (20). The Nencki-Symfonia EEG/ERP dataset. 10.82901/nemar.on004621

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.on004621.

Related & sibling datasets

ON005343EEG · 43 subj ON007454EEG · 42 subj ON005289EEG · 39 subj ON007427EEG · 44 subj ON006171EEG · 36 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.0.2

Sidecars

events · channels

Provenance

CC0 · 10.82901/nemar.on004621

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`ON004621`
Title	The Nencki-Symfonia EEG/ERP dataset
Author (year)	—
Canonical	—
Importable as	`ON004621`
Year	20
Authors	Dzianok Patrycja, Antonova Ingrida, Wojciechowski Jakub, Dreszer Joanna, Kublik Ewa
License	CC0
Citation / DOI	10.82901/nemar.on004621
Source links	OpenNeuro \| NeMAR \| Source URL

ON004621: eeg dataset, 42 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#