EEGdash›OpenNeuro›DS004011

Iss. 4011 · 22 subjects · 132 recordings · CC0

Dataset Brief · The nature of neural object representations during dynamic oc…

DS004011: meg dataset, 22 subjects#

The nature of neural object representations during dynamic occlusion

Citation: Lina Teichmann, Denise Moerel, Anina Rich, Chris Baker (2019). The nature of neural object representations during dynamic occlusion. 10.18112/openneuro.ds004011.v1.0.3

22-participant MEG dataset — The nature of neural object representations during dynamic occlusion.

Data & curation Lina Teichmann · Denise Moerel · Anina Rich · Chris Baker
Year 2019 · Distributed via OpenNeuro

MEG · 309 ch1200 HzBIDS 1.6.0Task · occlusionHealthyVisualPerception

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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},
}

§ 02Study · The README

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

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=22, range 22–41 yr, mean 24.7 yr)

20253040

Female · 17Male · 5

Sex composition

Female
19
Male
8
F : M ratio
2.38 : 1

70% female · n = 27 subjects with reported sex.

HandednessRight · 24Left · 3

Channel counts: 309 ch (n=132 recordings)

Sampling frequencies: 1200.0 Hz (n=132 recordings)

Total recording duration: 39 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 309 ch · MEG · 1200 Hz · 22 subjects, 132 recordings

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

NEMAR Processing Statistics#

The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.

HED event descriptors word cloud

§ 05Manifest · BIDS tree

Manifest#

File Explorer#

Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.DS004011(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Teichmann2022

Canonical—

Importable asDS004011 · Teichmann2022

Sourceeegdash/dataset/registry.py · [source ↗]

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

The nature of neural object representations during dynamic occlusion

Study:: ds004011 (OpenNeuro)
Author (year):: Teichmann2022
Canonical:: —

Also importable as: DS004011, Teichmann2022.

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 DOI: https://doi.org/10.18112/openneuro.ds004011.v1.0.3 NEMAR citation count: 1

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: str, overwrite: bool = False, offset: int = 0)[source]#

Save datasets to files by creating one subdirectory for each dataset:

path/
    0/
        0-raw.fif | 0-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
    1/
        1-raw.fif | 1-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)

Parameters:

path (str) –

Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FacePre-bundled mirror at EEGDash/ds004011 · pull with datasets.load_dataset("EEGDash/ds004011").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS004011.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.

Load one EEG recordingResolve a single record to an MNE Raw with channels and events.

EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.

Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.

Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.

Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for ds004011 to reproduce the tutorial on this dataset.

Citation

Lina Teichmann, Denise Moerel, Anina Rich, Chris Baker (2019). The nature of neural object representations during dynamic occlusion. 10.18112/openneuro.ds004011.v1.0.3

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds004011.v1.0.3.

Related & sibling datasets

DS003352MEG · 18 subj DS002712MEG · 25 subj DS006012MEG · 21 subj DS000117MEG · 17 subj DS004330MEG · 30 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.6.0

Sidecars

events · channels · coordsystem

Provenance

CC0 · 10.18112/openneuro.ds004011.v1.0.3

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS004011`
Title	The nature of neural object representations during dynamic occlusion
Author (year)	`Teichmann2022`
Canonical	—
Importable as	`DS004011`, `Teichmann2022`
Year	2019
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

DS004011: meg dataset, 22 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#