DS006159#

Implicit Learning EEG (BioSemi)

Access recordings and metadata through EEGDash.

Citation: Mateo Leganes-Fonteneau (2025). Implicit Learning EEG (BioSemi). 10.18112/openneuro.ds006159.v1.0.0

Modality: eeg Subjects: 61 Recordings: 551 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006159

dataset = DS006159(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS006159(cache_dir="./data", subject="01")

Advanced query

dataset = DS006159(
    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{ds006159,
  title = {Implicit Learning EEG (BioSemi)},
  author = {Mateo Leganes-Fonteneau},
  doi = {10.18112/openneuro.ds006159.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006159.v1.0.0},
}

About This Dataset#

References

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

DS006159

Title

Implicit Learning EEG (BioSemi)

Year

2025

Authors

Mateo Leganes-Fonteneau

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds006159.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds006159,
  title = {Implicit Learning EEG (BioSemi)},
  author = {Mateo Leganes-Fonteneau},
  doi = {10.18112/openneuro.ds006159.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006159.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: 61

  • Recordings: 551

  • Tasks: 1

Channels & sampling rate

  • Channels: 73

  • Sampling rate (Hz): 1024.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 14.3 GB

  • File count: 551

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds006159.v1.0.0

Provenance

API Reference#

Use the DS006159 class to access this dataset programmatically.

class eegdash.dataset.DS006159(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006159. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 62; recordings: 671; 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/ds006159 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006159

Examples

>>> from eegdash.dataset import DS006159
>>> dataset = DS006159(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#