DS007427: eeg dataset, 44 subjects#

Comprehensive methodology for sample enrichment in EEG biomarker studies for Alzheimer’s risk classification

Access recordings and metadata through EEGDash.

Citation: Verónica Henao Isaza, Carlos Andrés Tobón Quintero, John Fredy Ochoa Gómez (2026). Comprehensive methodology for sample enrichment in EEG biomarker studies for Alzheimer’s risk classification. 10.18112/openneuro.ds007427.v1.0.1

Modality: eeg Subjects: 44 Recordings: 44 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007427

dataset = DS007427(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS007427(cache_dir="./data", subject="01")

Advanced query

dataset = DS007427(
    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{ds007427,
  title = {Comprehensive methodology for sample enrichment in EEG biomarker studies for Alzheimer’s risk classification},
  author = {Verónica Henao Isaza and Carlos Andrés Tobón Quintero and John Fredy Ochoa Gómez},
  doi = {10.18112/openneuro.ds007427.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds007427.v1.0.1},
}

About This Dataset#

References

Henao Isaza, V., Aguillon, D., Tobón Quintero, C. A., Lopera, F., & Ochoa Gómez, J. F. (2026). Comprehensive methodology for sample enrichment in EEG biomarker studies for Alzheimer’s risk classification. PLOS ONE. https://doi.org/10.1371/journal.pone.0343722

Dataset Information#

Dataset ID

DS007427

Title

Comprehensive methodology for sample enrichment in EEG biomarker studies for Alzheimer’s risk classification

Author (year)

Isaza2026_Comprehensive

Canonical

HenaoIsaza2026

Importable as

DS007427, Isaza2026_Comprehensive, HenaoIsaza2026

Year

2026

Authors

Verónica Henao Isaza, Carlos Andrés Tobón Quintero, John Fredy Ochoa Gómez

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds007427.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds007427,
  title = {Comprehensive methodology for sample enrichment in EEG biomarker studies for Alzheimer’s risk classification},
  author = {Verónica Henao Isaza and Carlos Andrés Tobón Quintero and John Fredy Ochoa Gómez},
  doi = {10.18112/openneuro.ds007427.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds007427.v1.0.1},
}

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: 44

  • Recordings: 44

  • Tasks: 1

Channels & sampling rate

  • Channels: 60

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 3.911543333333333

Tags

  • Pathology: Dementia

  • Modality: Resting State

  • Type: Clinical/Intervention

Files & format

  • Size on disk: 3.1 GB

  • File count: 44

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds007427.v1.0.1

Provenance

API Reference#

Use the DS007427 class to access this dataset programmatically.

class eegdash.dataset.DS007427(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Comprehensive methodology for sample enrichment in EEG biomarker studies for Alzheimer’s risk classification

Study:

ds007427 (OpenNeuro)

Author (year):

Isaza2026_Comprehensive

Canonical:

HenaoIsaza2026

Also importable as: DS007427, Isaza2026_Comprehensive, HenaoIsaza2026.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Dementia. Subjects: 44; recordings: 44; tasks: 1.

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/ds007427 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007427 DOI: https://doi.org/10.18112/openneuro.ds007427.v1.0.1

Examples

>>> from eegdash.dataset import DS007427
>>> dataset = DS007427(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

See Also#