DS006012#

A geometric shape regularity effect in the human brain: MEG dataset

Access recordings and metadata through EEGDash.

Citation: Mathias Sablé-Meyer, Lucas Benjamin, Cassandra Potier Watkins, Chenxi He, Maxence Pajot, Théo Morfoisse, Fosca Al Roumi, Stanislas Dehaene (2025). A geometric shape regularity effect in the human brain: MEG dataset. 10.18112/openneuro.ds006012.v1.0.1

Modality: meg Subjects: 20 Recordings: 973 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006012

dataset = DS006012(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 = DS006012(cache_dir="./data", subject="01")

Advanced query

dataset = DS006012(
    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{ds006012,
  title = {A geometric shape regularity effect in the human brain: MEG dataset},
  author = {Mathias Sablé-Meyer and Lucas Benjamin and Cassandra Potier Watkins and Chenxi He and Maxence Pajot and Théo Morfoisse and Fosca Al Roumi and Stanislas Dehaene},
  doi = {10.18112/openneuro.ds006012.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds006012.v1.0.1},
}

About This Dataset#

A geometric shape regularity effect in the human brain: MEG dataset

Authors:

Mathias Sablé-Meyer * Lucas Benjamin * Cassandra Potier Watkins * Chenxi He

View full README

A geometric shape regularity effect in the human brain: MEG dataset

Authors:

Mathias Sablé-Meyer * Lucas Benjamin * Cassandra Potier Watkins * Chenxi He * Maxence Pajot * Théo Morfoisse * Fosca Al Roumi * Stanislas Dehaene

*Corresponding author: mathias.sable-meyer@ucl.ac.uk_

Abstract

The perception and production of regular geometric shapes is a characteristic trait of human cultures since prehistory, whose neural mechanisms are unknown. Behavioral studies suggest that humans are attuned to discrete regularities such as symmetries and parallelism, and rely on their combinations to encode regular geometric shapes in a compressed form. To identify the relevant brain systems and their dynamics, we collected functional MRI and magnetoencephalography data in both adults and six-year-olds during the perception of simple shapes such as hexagons, triangles and quadrilaterals. The results revealed that geometric shapes, relative to other visual categories, induce a hypoactivation of ventral visual areas and an overactivation of the intraparietal and inferior temporal regions also involved in mathematical processing, whose activation is modulated by geometric regularity. While convolutional neural networks captured the early visual activity evoked by geometric shapes, they failed to account for subsequent dorsal parietal and prefrontal signals, which could only be captured by discrete geometric features or by more advanced transformer models of vision. We propose that the perception of abstract geometric regularities engages an additional symbolic mode of visual perception.

Notes about this dataset

We separately share the fMRI dataset at https://openneuro.org/datasets/ds006010_. Below are some notes about the MEG dataset of N=20 participants:

The code for the analyses associated to https://doi.org/10.1101/2024.03.13.584141_ are provided at mathias-sm/AGeometricShapeRegularityEffectHumanBrain_. However, these analyses have been performed on pre-processed data _without_ this defacing steps. I am not publishing this raw data, but should there be discrepancies or problems coming from the defacing, I have a copy of the following information, which I may ask for permission to share in specific cases:
1. The original data
2. The seed used for the anonymization procedure
3. The shuffling information.
Anonymization (including defacing of the anat folder) has been performed using the following command: python -c 'import mne_bids; mne_bids.anonymize_dataset("<input>", "<output>", random_state=<number>, daysback=<number>)' This has shuffled the participant order, changed the dates, defaced the anatomy, and stripped gender information from the dataset.
The data was pre-processed with the configuration file provided at mathias-sm/AGeometricShapeRegularityEffectHumanBrain_ for mne-bids-pipeline with the development version at the time, bce60a79241731bdd03fccffa6cf315a35b33ab2 on mne-tools/mne-bids-pipeline_

Dataset Information#

Dataset ID	`DS006012`
Title	A geometric shape regularity effect in the human brain: MEG dataset
Year	2025
Authors	Mathias Sablé-Meyer, Lucas Benjamin, Cassandra Potier Watkins, Chenxi He, Maxence Pajot, Théo Morfoisse, Fosca Al Roumi, Stanislas Dehaene
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006012.v1.0.1
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: 20
Recordings: 973
Tasks: 2

Channels & sampling rate

Channels: 306 (173), 336 (172), 333
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS006012 class to access this dataset programmatically.

class eegdash.dataset.DS006012(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006012. Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 21; recordings: 193; 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/ds006012 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006012

Examples

>>> from eegdash.dataset import DS006012
>>> dataset = DS006012(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