DS004796#

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

Access recordings and metadata through EEGDash.

Citation: Dzianok Patrycja, Kublik Ewa (2023). A Polish Electroencephalography, Alzheimer’s Risk-genes, Lifestyle and Neuroimaging (PEARL-Neuro) Database. 10.18112/openneuro.ds004796.v1.1.0

Modality: eeg Subjects: 79 Recordings: 2056 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

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},
}

About This Dataset#

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

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.

Data Descriptor:

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

View full README

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

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.

Data Descriptor:

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

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.

Dataset Information#

Dataset ID	`DS004796`
Title	A Polish Electroencephalography, Alzheimer’s Risk-genes, Lifestyle and Neuroimaging (PEARL-Neuro) Database
Year	2023
Authors	Dzianok Patrycja, Kublik Ewa
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004796.v1.1.0
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: 79
Recordings: 2056
Tasks: 3

Channels & sampling rate

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

API Reference#

Use the DS004796 class to access this dataset programmatically.

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

Bases: EEGDashDataset

OpenNeuro dataset ds004796. Modality: eeg; Experiment type: Memory/Resting state; Subject type: Other. 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

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, 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