DS004398#

planmemreplay

Access recordings and metadata through EEGDash.

Citation: G. Elliott Wimmer, Yunzhe Liu, Daniel C. McNamee, Raymond J. Dolan (2023). planmemreplay. 10.18112/openneuro.ds004398.v1.0.0

Modality: meg Subjects: 1 Recordings: 18 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004398

dataset = DS004398(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004398(cache_dir="./data", subject="01")

Advanced query

dataset = DS004398(
    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{ds004398,
  title = {planmemreplay},
  author = {G. Elliott Wimmer and Yunzhe Liu and Daniel C. McNamee and Raymond J. Dolan},
  doi = {10.18112/openneuro.ds004398.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004398.v1.0.0},
}

About This Dataset#

The MEG files contain a channel with triggers necessary for event marking and timing. Separate event files with onsets are provided in the participant directories for completeness only; the MEG triggers should be used for actual onsets in analysis. The delay between the trigger and the visual onset of an on-screen event sent by the projector is approximately 20 ms, as estimated using a photodiode.

Localizer phase triggers: [Info to be added]

Struct and Rew phase triggers: [Info to be added]

Post triggers: [Info to be added]

Dataset Information#

Dataset ID

DS004398

Title

planmemreplay

Year

2023

Authors

  1. Elliott Wimmer, Yunzhe Liu, Daniel C. McNamee, Raymond J. Dolan

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004398.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004398,
  title = {planmemreplay},
  author = {G. Elliott Wimmer and Yunzhe Liu and Daniel C. McNamee and Raymond J. Dolan},
  doi = {10.18112/openneuro.ds004398.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004398.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: 1

  • Recordings: 18

  • Tasks: 1

Channels & sampling rate

  • Channels: 272

  • Sampling rate (Hz): 600.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 1.3 GB

  • File count: 18

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004398.v1.0.0

Provenance

API Reference#

Use the DS004398 class to access this dataset programmatically.

class eegdash.dataset.DS004398(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004398. Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 1; recordings: 1; 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/ds004398 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004398

Examples

>>> from eegdash.dataset import DS004398
>>> dataset = DS004398(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#