DS005363#

Object recognition in healthy aging (ORHA) - EEG

Access recordings and metadata through EEGDash.

Citation: Marleen Haupt, Douglas D. Garrett, Radoslaw M. Cichy (2024). Object recognition in healthy aging (ORHA) - EEG. 10.18112/openneuro.ds005363.v1.0.0

Modality: eeg Subjects: 43 Recordings: 306 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005363

dataset = DS005363(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS005363(cache_dir="./data", subject="01")

Advanced query

dataset = DS005363(
    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{ds005363,
  title = {Object recognition in healthy aging (ORHA) - EEG},
  author = {Marleen Haupt and Douglas D. Garrett and Radoslaw M. Cichy},
  doi = {10.18112/openneuro.ds005363.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005363.v1.0.0},
}

About This Dataset#

This dataset contains the raw EEG data accompanying the paper “Healthy aging delays and dedifferentiates high-level visual representations”. Please cite the above paper if you use this data. The dataset includes: Brainvision files (.eeg, .vhdr, .vmrk) for all participants. The events files contain the onsets, durations, trial types and values for all trials in the corresponding run. Stimuli are images presented on a grey background with a central fixation: images of faces = S1-16 images of animals = S17-32 images of places = S33-48 images of objects = S49-64 catch trials = S65-69 Other triggers: button_press = S99 run_onset = S100+run_number (8 runs in total) run_end = S199 For a full description of the paradigm and the employed procedures please see the paper.

References for MNE BIDS conversion

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8

Dataset Information#

Dataset ID

DS005363

Title

Object recognition in healthy aging (ORHA) - EEG

Year

2024

Authors

Marleen Haupt, Douglas D. Garrett, Radoslaw M. Cichy

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds005363.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds005363,
  title = {Object recognition in healthy aging (ORHA) - EEG},
  author = {Marleen Haupt and Douglas D. Garrett and Radoslaw M. Cichy},
  doi = {10.18112/openneuro.ds005363.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005363.v1.0.0},
}

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: 43

  • Recordings: 306

  • Tasks: 1

Channels & sampling rate

  • Channels: 64

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 0.0

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Perception

Files & format

  • Size on disk: 17.7 GB

  • File count: 306

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds005363.v1.0.0

Provenance

API Reference#

Use the DS005363 class to access this dataset programmatically.

class eegdash.dataset.DS005363(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005363. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 43; recordings: 43; 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/ds005363 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005363

Examples

>>> from eegdash.dataset import DS005363
>>> dataset = DS005363(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#