EEGdash›OpenNeuro›DS004796

Iss. 4796 · 79 subjects · 235 recordings · CC0

Dataset Brief · A Polish Electroencephalography, Alzheimer’s Risk-genes, Life…

DS004796: eeg dataset, 79 subjects#

A Polish Electroencephalography, Alzheimer’s Risk-genes, Lifestyle and Neuroimaging (PEARL-Neuro) Database

Citation: Dzianok Patrycja, Kublik Ewa (2024). A Polish Electroencephalography, Alzheimer’s Risk-genes, Lifestyle and Neuroimaging (PEARL-Neuro) Database. 10.18112/openneuro.ds004796.v1.1.0

79-participant EEG dataset — A Polish Electroencephalography, Alzheimer’s Risk-genes, Lifestyle and Neuroimaging (PEARL-Neuro) Database.

Data & curation Dzianok Patrycja · Kublik Ewa
Year 2024 · Distributed via OpenNeuro
Funding Polish National Science Center (NCN) grant no. 2018/31/N/HS6/03551

EEG · 127 ch1000 HzBIDS 1.0.23 tasksOtherVisual/Resting StateMemory/Resting state

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 DS004796

dataset = DS004796(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 = DS004796(cache_dir="./data", subject="01")

Advanced query

dataset = DS004796(
    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{ds004796,
  title = {A Polish Electroencephalography, Alzheimer’s Risk-genes, Lifestyle and Neuroimaging (PEARL-Neuro) Database},
  author = {Dzianok Patrycja and Kublik Ewa},
  doi = {10.18112/openneuro.ds004796.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds004796.v1.1.0},
}

§ 02Study · The README

About This Dataset#

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.

* doi.org/10.1038/s41597-024-03106-5 (https://www.nature.com/articles/s41597-024-03106-5)

A Polish Electroencephalography, Alzheimer’s Risk-genes, Lifestyle and Neuroimaging (PEARL-Neuro) Database

Please cite the following reference if you use these data:

* Dzianok P, Kublik E. PEARL-Neuro Database: EEG, fMRI, health and lifestyle data of middle-aged people at risk of dementia. Sci Data 11, 276 (2024). DOI: https://doi.org/10.1038/s41597-024-03106-5

Publications related to this dataset, reporting & additional data

* PTDZ/PEARL-Neuro — updates, additional study details, and list of research outputs related to this dataset.

View full README

A Polish Electroencephalography, Alzheimer’s Risk-genes, Lifestyle and Neuroimaging (PEARL-Neuro) Database

Please cite the following reference if you use these data:

* Dzianok P, Kublik E. PEARL-Neuro Database: EEG, fMRI, health and lifestyle data of middle-aged people at risk of dementia. Sci Data 11, 276 (2024). DOI: https://doi.org/10.1038/s41597-024-03106-5

Publications related to this dataset, reporting & additional data

* PTDZ/PEARL-Neuro — updates, additional study details, and list of research outputs related to this dataset.

IMPORTANT: Please inform us of any research outputs related to the shared data, including publications, preprints, posters, abstracts, talks, and any commercial usage. This is crucial for ensuring transparency and informing users about the analyses already performed on this dataset. Additionally, such information can foster collaboration.

Description of the database:

Full cohort: 192 healthy middle-aged (50-63) individuals, balanced female and male ratio. * Genetic data (N = 192):

Apolipoprotein E (APOE)

Phosphatidylinositol binding clathrin assembly protein (PICALM)

* Basic demographic and health data * Psychometric data (memory, intelligence, mood, personality, stress coping strategies)

Cohort subgroup: 79 healthy middle-aged (50-63) individuals, balanced female and male ratio. * Neuroimaging data:

Functional data — electroencefalography (EEG) and functional magnetic resonance imaging (fMRI): * Resting-state protocol (with two conditions: eyes open and eyes closed) * Cognitive tasks: multi-source interference task (MSIT) and Sternberg’s memory task

* Blood tests data (blood count, lipid profile, HSV virus)

Release history:

* 10/2023: Initial release * 02/2024: Public release * 06/2025, version: 1.1.0 — marker corrections in .tsv and .vmrk EEG resting-state files

During EEG data acquisition, technical issues led to missing starting markers for the eyes-open and/or eyes-closed conditions in the resting-state protocol for some participants. As described in the Data Note, the S1 marker indicates the end of the instruction phase—when the participant presses “Enter” to begin a condition (either eyes-open or eyes-closed). Consequently, the first S1 marker coincides with the S2 marker (start of the eyes-open condition), and the second S1 marker aligns with the S4 marker (start of the eyes-closed condition).

To ensure consistency with Table 5 in the released Data Note, the missing markers were added to the relevant files (.tsv and .vmrk) for the following participants: 08, 09, 11, 12, 14, 15, 21, 22, 25, 35, 42, 54, 62, 64, 65, 67, 70, 71, 73, 75, and 79. For participants 19 and 30, the S11 marker (indicating the end of the task and accompanying sound effect) was not saved, resulting in a slightly shorter eyes-closed recording duration (by approximately 30–60 seconds).

For participant 34, the S11 marker was also not recorded because he/she forgot to press “Enter” to mark the start of the eyes-closed condition, pressing it only after the condition had ended. However, he/she followed the instructions and kept his/her eyes closed during the condition. Therefore, the relevant markers (S1/S4) were manually adjusted to reflect the correct start time.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=79, range 50–63 yr, mean 55.1 yr)

505560

Other · 79

Sex composition

Other
192

Channel counts: 127 ch (n=235 recordings)

Sampling frequencies: 1000.0 Hz (n=235 recordings)

Total recording duration: 44 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 127 ch · EEG · 1000 Hz · 79 subjects, 235 recordings

Live trace viewer — sub-13 · task-msit

Showing one representative recording out of 79 subjects and 235 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.DS004796(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Patrycja2023_Polish

Canonical—

Importable asDS004796 · Patrycja2023_Polish

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

class eegdash.dataset.DS004796(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

A Polish Electroencephalography, Alzheimer’s Risk-genes, Lifestyle and Neuroimaging (PEARL-Neuro) Database

Study:: ds004796 (OpenNeuro)
Author (year):: Patrycja2023_Polish
Canonical:: —

Also importable as: DS004796, Patrycja2023_Polish.

Modality: eeg. Subjects: 79; recordings: 235; 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/ds004796 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004796 DOI: https://doi.org/10.18112/openneuro.ds004796.v1.1.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004796
>>> dataset = DS004796(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 FacePre-bundled mirror at EEGDash/ds004796 · pull with datasets.load_dataset("EEGDash/ds004796").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS004796.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 ds004796 to reproduce the tutorial on this dataset.

Citation

Dzianok Patrycja, Kublik Ewa (2024). A Polish Electroencephalography, Alzheimer’s Risk-genes, Lifestyle and Neuroimaging (PEARL-Neuro) Database. 10.18112/openneuro.ds004796.v1.1.0

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds004796.v1.1.0.

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+ 1 more — see See Also below →

BIDS

BIDS 1.0.2

Sidecars

events

Provenance

CC0 · 10.18112/openneuro.ds004796.v1.1.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS004796`
Title	A Polish Electroencephalography, Alzheimer’s Risk-genes, Lifestyle and Neuroimaging (PEARL-Neuro) Database
Author (year)	`Patrycja2023_Polish`
Canonical	—
Importable as	`DS004796`, `Patrycja2023_Polish`
Year	2024
Authors	Dzianok Patrycja, Kublik Ewa
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004796.v1.1.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS004796: eeg dataset, 79 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#