DS004330#

The spatiotemporal neural dynamics of object recognition for natural images and line drawings (MEG)

Access recordings and metadata through EEGDash.

Citation: Johannes J.D. Singer, Radoslaw M. Cichy, Martin N. Hebart (2022). The spatiotemporal neural dynamics of object recognition for natural images and line drawings (MEG). 10.18112/openneuro.ds004330.v1.0.0

Modality: meg Subjects: 30 Recordings: 1145 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004330

dataset = DS004330(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 = DS004330(cache_dir="./data", subject="01")

Advanced query

dataset = DS004330(
    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{ds004330,
  title = {The spatiotemporal neural dynamics of object recognition for natural images and line drawings (MEG)},
  author = {Johannes J.D. Singer and Radoslaw M. Cichy and Martin N. Hebart},
  doi = {10.18112/openneuro.ds004330.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004330.v1.0.0},
}

About This Dataset#

This dataset contains the raw MEG data accompanying the paper “The spatiotemporal neural dynamics of object recognition for natural images and line drawings” (Link to preprint: https://biorxiv.org/cgi/content/short/2022.08.12.503484v1). Please cite the above paper if you use this data.

The dataset includes:

MEG data for 9 runs for each subjects. Events files that contain the onsets, durations and trial types for each trial in the experiment (excluding catch trials).

For a full description of the paradigm and the employed procedures please see the manuscript.

Results for the first-level analyses for this data can be found on OSF (https://osf.io/vsc6y/). Code for the analysis of the data can be found on Github (Singerjohannes/object_drawing_dynamics).

Dataset Information#

Dataset ID	`DS004330`
Title	The spatiotemporal neural dynamics of object recognition for natural images and line drawings (MEG)
Year	2022
Authors	Johannes J.D. Singer, Radoslaw M. Cichy, Martin N. Hebart
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004330.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: 30
Recordings: 1145
Tasks: 1

Channels & sampling rate

Channels: 306 (270), 310 (270)
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS004330 class to access this dataset programmatically.

class eegdash.dataset.DS004330(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004330. Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 30; recordings: 270; 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/ds004330 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004330

Examples

>>> from eegdash.dataset import DS004330
>>> dataset = DS004330(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