DS002761#

memoryreplay

Access recordings and metadata through EEGDash.

Citation: G. Elliott Wimmer, Yunzhe Liu, Neža Vehar, Timothy E.J. Behrens, Raymond J. Dolan (2020). memoryreplay. 10.18112/openneuro.ds002761.v1.1.2

Modality: meg Subjects: 25 Recordings: 752 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS002761

dataset = DS002761(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 = DS002761(cache_dir="./data", subject="01")

Advanced query

dataset = DS002761(
    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{ds002761,
  title = {memoryreplay},
  author = {G. Elliott Wimmer and Yunzhe Liu and Neža Vehar and Timothy E.J. Behrens and Raymond J. Dolan},
  doi = {10.18112/openneuro.ds002761.v1.1.2},
  url = {https://doi.org/10.18112/openneuro.ds002761.v1.1.2},
}

About This Dataset#

The MEG files contain a channel with triggers necessary for event marking and timing. Separate event files with onsets are provided in the participant directories for completeness only; the MEG triggers should be used for actual onsets in analysis. The delay between the trigger and the visual onset of an on-screen event sent by the projector is approximately 20 ms, as estimated using a photodiode.

Memory phase triggers: At the onset of a trial, the first trigger represents the category (1-8) of the on-screen image. Categories 1-6 represent actual stimulus categories. Trigger values of 7 and 8 represent the 4 positive and 4 negative story-ending stimuli, respectively. The onset of the answer, approximately 5.5 sec later, is marked by a trigger value of 11.

Localizer phase triggers: As in the memory phase, at the onset of a trial, the first trigger represents the category (1-8) of the on-screen image. Categories 1-6 represent true categories. Trigger values of 7 and 8 represent the 4 positive and 4 negative story-ending stimuli, respectively. For a baseline, note that for the 2 s prior to picture onset, a word naming that picture was presented on the screen; thus, baseline values should be taken from data more than 2 s before the trigger onset.

Methods note: a sequenceness analysis step was omitted from the published 2020 Nature Neuroscience paper. The text should have read: “We next asked whether the βi(Δt) was consistent with a specified 6 × 6 transition matrix by taking the Frobenius inner product between these two matrices (the sum of element-wise products of the two matrices). This resulted in a single number ZΔt, which pertained to lag Δt. For each trial, sequenceness results were then z-scored across lags. Finally, differential forward – backward sequenceness was defined as ZfΔt − ZbΔt.”

Dataset Information#

Dataset ID	`DS002761`
Title	memoryreplay
Year	2020
Authors	Elliott Wimmer, Yunzhe Liu, Neža Vehar, Timothy E.J. Behrens, Raymond J. Dolan
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds002761.v1.1.2
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: 25
Recordings: 752
Tasks: 1

Channels & sampling rate

Channels: 273
Sampling rate (Hz): 600.0
Duration (hours): 0.0

API Reference#

Use the DS002761 class to access this dataset programmatically.

class eegdash.dataset.DS002761(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds002761. Modality: meg; Experiment type: Memory; Subject type: Healthy. Subjects: 25; recordings: 249; 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/ds002761 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002761

Examples

>>> from eegdash.dataset import DS002761
>>> dataset = DS002761(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