DS004483#

ABSeqMEG

Access recordings and metadata through EEGDash.

Citation: Samuel Planton*, Fosca Al Roumi*, Liping Wang, Stanislas Dehaene (2023). ABSeqMEG. 10.18112/openneuro.ds004483.v1.0.0

Modality: meg Subjects: 19 Recordings: 1384 License: CC0 Source: openneuro Citations: 2.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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.

Dataset Information#

Dataset ID	`DS004483`
Title	ABSeqMEG
Year	2023
Authors	Samuel Planton, Fosca Al Roumi, Liping Wang, Stanislas Dehaene
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004483.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: 19
Recordings: 1384
Tasks: 1

Channels & sampling rate

Channels: 396 (263), 60 (263)
Sampling rate (Hz): 250.0
Duration (hours): 0.0

API Reference#

Use the DS004483 class to access this dataset programmatically.

class eegdash.dataset.DS004483(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004483. 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

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/ds004483 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004483

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, 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