DS006374#

Expectation effects on repetition suppression in nociception

Access recordings and metadata through EEGDash.

Citation: Lisa-Marie Pohle, Moritz Nickel, Birgit Nierula, Markus Ploner, Ulrike Horn, Falk Eippert (2025). Expectation effects on repetition suppression in nociception. 10.18112/openneuro.ds006374.v1.0.0

Modality: eeg Subjects: 36 Recordings: 3671 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006374

dataset = DS006374(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 = DS006374(cache_dir="./data", subject="01")

Advanced query

dataset = DS006374(
    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{ds006374,
  title = {Expectation effects on repetition suppression in nociception},
  author = {Lisa-Marie Pohle and Moritz Nickel and Birgit Nierula and Markus Ploner and Ulrike Horn and Falk Eippert},
  doi = {10.18112/openneuro.ds006374.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006374.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	`DS006374`
Title	Expectation effects on repetition suppression in nociception
Year	2025
Authors	Lisa-Marie Pohle, Moritz Nickel, Birgit Nierula, Markus Ploner, Ulrike Horn, Falk Eippert
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006374.v1.0.0
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: 36
Recordings: 3671
Tasks: 3

Channels & sampling rate

Channels: 35 (358), 32 (358)
Sampling rate (Hz): 2000.0
Duration (hours): 0.0

API Reference#

Use the DS006374 class to access this dataset programmatically.

class eegdash.dataset.DS006374(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006374. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 36; recordings: 358; tasks: 2.

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/ds006374 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006374

Examples

>>> from eegdash.dataset import DS006374
>>> dataset = DS006374(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