EEGdash›OpenNeuro›DS006012

Iss. 6012 · 21 subjects · 193 recordings · CC0

Dataset Brief · A geometric shape regularity effect in the human brain

DS006012: meg dataset, 21 subjects#

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

Citation: Mathias Sablé-Meyer, Lucas Benjamin, Cassandra Potier Watkins, Chenxi He, Maxence Pajot, Théo Morfoisse, Fosca Al Roumi, Stanislas Dehaene (20). A geometric shape regularity effect in the human brain: MEG dataset. 10.18112/openneuro.ds006012.v1.0.1

21-participant MEG dataset — A geometric shape regularity effect in the human brain: MEG dataset.

Data & curation Mathias Sablé-Meyer · Lucas Benjamin · Cassandra Potier Watkins · Chenxi He · Maxence Pajot · Théo Morfoisse · …
Year 20 · Distributed via OpenNeuro
Funding ERC-ADG-2015 NeuroSyntax to Stanislas Dehaene · CEA to Stanislas Dehaene · + 3 more

MEG · 336 (172), 333 ch1000 HzBIDS 1.6.02 tasks15 sessionsHealthyVisualPerception

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 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},
}

§ 02Study · The README

About This 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

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

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:

View full README

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

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:

The original data

The seed used for the anonymization procedure

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

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=20, range 20–42 yr, mean 26.1 yr)

20253040

Other · 20

Channel counts (ch)

333336

Sampling frequencies: 1000.0 Hz (n=173 recordings)

Total recording duration: 15 h 35 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 336 (172), 333 ch · MEG · 1000 Hz · 21 subjects, 193 recordings

Live trace viewer — sub-13 · task-POGS · run-06

Showing one representative recording out of 21 subjects and 193 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _meg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?meg=<url>) to inspect it.

Electrode layout — MEG · 306 sensors — 306 channels

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.DS006012(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)SableMeyer2025

Canonical—

Importable asDS006012 · SableMeyer2025

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

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

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

Study:: ds006012 (OpenNeuro)
Author (year):: SableMeyer2025
Canonical:: —

Also importable as: DS006012, SableMeyer2025.

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 DOI: https://doi.org/10.18112/openneuro.ds006012.v1.0.1

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: 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/ds006012 · pull with datasets.load_dataset("EEGDash/ds006012").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS006012.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 ds006012 to reproduce the tutorial on this dataset.

Citation

Mathias Sablé-Meyer, Lucas Benjamin, Cassandra Potier Watkins, Chenxi He, Maxence Pajot, … (20). A geometric shape regularity effect in the human brain: MEG dataset. 10.18112/openneuro.ds006012.v1.0.1

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds006012.v1.0.1.

Related & sibling datasets

DS003352MEG · 18 subj DS002712MEG · 25 subj DS004330MEG · 30 subj DS004011MEG · 22 subj DS000117MEG · 17 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.6.0

Sidecars

coordsystem

Provenance

CC0 · 10.18112/openneuro.ds006012.v1.0.1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS006012`
Title	A geometric shape regularity effect in the human brain: MEG dataset
Author (year)	`SableMeyer2025`
Canonical	—
Importable as	`DS006012`, `SableMeyer2025`
Year	20
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

DS006012: meg dataset, 21 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#