DS004483: meg dataset, 19 subjects#
ABSeqMEG
Citation: Samuel Planton*, Fosca Al Roumi*, Liping Wang, Stanislas Dehaene (20). ABSeqMEG. 10.18112/openneuro.ds004483.v1.0.0
19-participant MEG dataset — ABSeqMEG.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004483
dataset = DS004483(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004483(cache_dir="./data", subject="01")
Advanced query
dataset = DS004483(
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{ds004483,
title = {ABSeqMEG},
author = {Samuel Planton* and Fosca Al Roumi* and Liping Wang and Stanislas Dehaene},
doi = {10.18112/openneuro.ds004483.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004483.v1.0.0},
}
About This Dataset#
This dataset contains the MEG data from the article entitled Compression of binary sound sequences in human working memory https://www.biorxiv.org/content/10.1101/2022.10.15.512361v1
According to the language of thought hypothesis, regular sequences are compressed in human working memory using recursive loops akin to a mental program that predicts future items. We tested this theory by probing working memory for 16-item sequences made of two sounds. We recorded brain activity with functional MRI and magneto-encephalography (MEG) while participants listened to a hierarchy of sequences of variable complexity, whose minimal description required transition probabilities, chunking, or nested structures. Occasional deviant sounds probed the participants’ knowledge of the sequence. We predicted that task difficulty and brain activity would be proportional to minimal description length (MDL) in our formal language. Furthermore, activity should increase with MDL for learned sequences, and decrease with MDL for deviants. These predictions were upheld in both fMRI and MEG, indicating that sequence predictions are highly dependent on sequence structure and become weaker and delayed as complexity increases. The proposed language recruited bilateral superior temporal, precentral, anterior intraparietal and cerebellar cortices. These regions overlapped extensively with a localizer for mathematical calculation, and much less with spoken or written language processing. We propose that these areas collectively encode regular sequences as repetitions with variations and their recursive composition into nested structures.
Cohort#
Dataset Statistics#
Age distribution by gender (n=14, range 18–35 yr, mean 25.6 yr)
Channel counts: 396 ch (n=263 recordings)
Sampling frequencies: 250.0 Hz (n=263 recordings)
Total recording duration: 16 h 40 min
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-abseq · run-04
Showing one representative recording out of
19 subjects and 282 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
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.
Full dataset metadata table
Dataset ID |
|
Title |
ABSeqMEG |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Samuel Planton*, Fosca Al Roumi*, Liping Wang, Stanislas Dehaene |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004483,
title = {ABSeqMEG},
author = {Samuel Planton* and Fosca Al Roumi* and Liping Wang and Stanislas Dehaene},
doi = {10.18112/openneuro.ds004483.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004483.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004483 · Planton2023eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004483(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
ABSeqMEG
- Study:
ds004483(OpenNeuro)- Author (year):
Planton2023- Canonical:
—
Also importable as:
DS004483,Planton2023.Modality:
meg; Experiment type:Memory; Subject type:Healthy. Subjects: 19; recordings: 282; 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/ds004483 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004483 DOI: https://doi.org/10.18112/openneuro.ds004483.v1.0.0 NEMAR citation count: 2
Examples
>>> from eegdash.dataset import DS004483 >>> dataset = DS004483(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.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds004483").huggingface
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 ds004483 to reproduce the tutorial on this dataset.
Citation
Samuel Planton, Fosca Al Roumi, Liping Wang, Stanislas Dehaene (20). ABSeqMEG. 10.18112/openneuro.ds004483.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004483.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset