DS004621#

The Nencki-Symfonia EEG/ERP dataset

Access recordings and metadata through EEGDash.

Citation: Dzianok Patrycja, Antonova Ingrida, Wojciechowski Jakub, Dreszer Joanna, Kublik Ewa (2023). The Nencki-Symfonia EEG/ERP dataset. 10.18112/openneuro.ds004621.v1.0.4

Modality: eeg Subjects: 42 Recordings: 848 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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

Dataset Information#

Dataset ID	`DS004621`
Title	The Nencki-Symfonia EEG/ERP dataset
Year	2023
Authors	Dzianok Patrycja, Antonova Ingrida, Wojciechowski Jakub, Dreszer Joanna, Kublik Ewa
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004621.v1.0.4
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 42
Recordings: 848
Tasks: 4

Channels & sampling rate

Channels: 127
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS004621 class to access this dataset programmatically.

class eegdash.dataset.DS004621(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004621. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. 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/ds004621 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004621

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None