DS004621: eeg dataset, 42 subjects#
The Nencki-Symfonia EEG/ERP dataset
Citation: Dzianok Patrycja, Antonova Ingrida, Wojciechowski Jakub, Dreszer Joanna, Kublik Ewa (20). The Nencki-Symfonia EEG/ERP dataset. 10.18112/openneuro.ds004621.v1.0.4
42-participant EEG dataset — The Nencki-Symfonia EEG/ERP dataset.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004621
dataset = DS004621(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004621(cache_dir="./data", subject="01")
Advanced query
dataset = DS004621(
cache_dir="./data",
query={"subject": {"$in": ["01", "02"]}},
)
Iterate recordings
for rec in dataset:
print(rec.subject, rec.raw.info['sfreq'])
If you use this dataset in your research, please cite the original authors.
BibTeX
@dataset{ds004621,
title = {The Nencki-Symfonia EEG/ERP dataset},
author = {Dzianok Patrycja and Antonova Ingrida and Wojciechowski Jakub and Dreszer Joanna and Kublik Ewa},
doi = {10.18112/openneuro.ds004621.v1.0.4},
url = {https://doi.org/10.18112/openneuro.ds004621.v1.0.4},
}
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
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
Cohort#
Dataset Statistics#
Age distribution by gender (n=42, range 20–34 yr, mean 24.6 yr)
Sex composition
Channel counts: 127 ch (n=167 recordings)
Sampling frequencies: 1000.0 Hz (n=167 recordings)
Total recording duration: 45 h
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-msit
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
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.
Full dataset metadata table
Dataset ID |
|
Title |
The Nencki-Symfonia EEG/ERP dataset |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Dzianok Patrycja, Antonova Ingrida, Wojciechowski Jakub, Dreszer Joanna, Kublik Ewa |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004621,
title = {The Nencki-Symfonia EEG/ERP dataset},
author = {Dzianok Patrycja and Antonova Ingrida and Wojciechowski Jakub and Dreszer Joanna and Kublik Ewa},
doi = {10.18112/openneuro.ds004621.v1.0.4},
url = {https://doi.org/10.18112/openneuro.ds004621.v1.0.4},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004621 · Patrycja2023_Nenckieegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004621(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
The Nencki-Symfonia EEG/ERP dataset
- Study:
ds004621(OpenNeuro)- Author (year):
Patrycja2023_Nencki- Canonical:
—
Also importable as:
DS004621,Patrycja2023_Nencki.Modality:
eeg. 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/ds004621 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004621 DOI: https://doi.org/10.18112/openneuro.ds004621.v1.0.4 NEMAR citation count: 1
Examples
>>> from eegdash.dataset import DS004621 >>> dataset = DS004621(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.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds004621").huggingface
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 ds004621 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. 10.18112/openneuro.ds004621.v1.0.4
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004621.v1.0.4.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset