DS004011#

The nature of neural object representations during dynamic occlusion

Access recordings and metadata through EEGDash.

Citation: Lina Teichmann, Denise Moerel, Anina Rich, Chris Baker (2022). The nature of neural object representations during dynamic occlusion. 10.18112/openneuro.ds004011.v1.0.3

Modality: meg Subjects: 22 Recordings: 132 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004011

dataset = DS004011(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004011(cache_dir="./data", subject="01")

Advanced query

dataset = DS004011(
    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{ds004011,
  title = {The nature of neural object representations during dynamic occlusion},
  author = {Lina Teichmann and Denise Moerel and Anina Rich and Chris Baker},
  doi = {10.18112/openneuro.ds004011.v1.0.3},
  url = {https://doi.org/10.18112/openneuro.ds004011.v1.0.3},
}

About This Dataset#

The main folder contains the raw MEG data for all participants in standard bids format. See references. The ‘sourcedata’ folder contains the behavioural data collected during the MEG session as well as the eyetracking data. The data in this folder follows the following trial structure: - sourcedata

  • beh
    • sub-[participant number]

      • sub-[participant number]_task-occlusion_run-[run number]_events.csv: contains all the events for each trial in the MEG session, detailing what was shown on the screen.

      • sub-[participant number]_task-occlusion_run-[run number]_occframes.csv: contains all the stimulus positions for each occlusion trial in the MEG session.

      • sub-[participant number]_task-occlusion_run-[run number]_disframes.csv: contains all the stimulus positions for each disappearance trial in the MEG session.

  • eyetracking

    • sub-[participant number]_Occ.edf: edf file containing the eye positions during the MEG session.

The ‘derivatives’ folder contains the pre-processed MEG data for each participant. The data in this folder follows the following trial structure: - derivatives

  • preprocessed
    • cosmo_p[participant number].mat: cosmomvpa formatted file with the pre-processed data, epoched for each trial, containing the following variables:

      • ds_diss: cosmo data struct containing the disappearance trials epoched relative to stimulus onset (MEG channels)

      • ds_occ: cosmo data struct containing the disappearance trials epoched relative to stimulus onset (MEG channels)

      • ds_loc: cosmo data struct containing the unpredictable position stream trials epoched relative to stimulus onset (MEG channels)

      • ds_eyes_diss: cosmo data struct containing the disappearance trials epoched relative to stimulus onset (eye-x, eye-y, pupil size)

      • ds_eyes_occ: cosmo data struct containing the disappearance trials epoched relative to stimulus onset (eye-x, eye-y, pupil size)

      • ds_eyes_loc: cosmo data struct containing the unpredictable position stream trials epoched relative to stimulus onset (eye-x, eye-y, pupil size)

    •  cosmo_p[participant number]_position_epochs.mat: cosmomvpa formatted file with the pre-processed data, epoched relative to each position change, containing the following variables:

      • ds_tiny: cell with two entries. First entry contains the disappearance trials epoched relative to position change. Second entry contains the occlusion trials epoched relative to position change. (MEG channels)

      • ds_tiny_eyes: cell with two entries. First entry contains the disappearance trials epoched relative to position change. Second entry contains the occlusion trials epoched relative to position change. (eye-x, eye-y, pupil size)

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 Niso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfort, A., Henson, R. N., Jas, M., Litvak, V., Moreau, J., Oostenveld, R., Schoffelen, J., Tadel, F., Wexler, J., Baillet, S. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Scientific Data, 5, 180110. https://doi.org/10.1038/sdata.2018.110

Dataset Information#

Dataset ID

DS004011

Title

The nature of neural object representations during dynamic occlusion

Year

2022

Authors

Lina Teichmann, Denise Moerel, Anina Rich, Chris Baker

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004011.v1.0.3

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004011,
  title = {The nature of neural object representations during dynamic occlusion},
  author = {Lina Teichmann and Denise Moerel and Anina Rich and Chris Baker},
  doi = {10.18112/openneuro.ds004011.v1.0.3},
  url = {https://doi.org/10.18112/openneuro.ds004011.v1.0.3},
}

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: 22

  • Recordings: 132

  • Tasks: 1

Channels & sampling rate

  • Channels: 271 (132), 309 (132)

  • Sampling rate (Hz): 1200.0

  • Duration (hours): 0.0

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Perception

Files & format

  • Size on disk: 198.1 GB

  • File count: 132

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004011.v1.0.3

Provenance

API Reference#

Use the DS004011 class to access this dataset programmatically.

class eegdash.dataset.DS004011(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004011. Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 22; recordings: 132; 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/ds004011 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004011

Examples

>>> from eegdash.dataset import DS004011
>>> dataset = DS004011(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#