DS002761: meg dataset, 25 subjects#
memoryreplay
Citation: G. Elliott Wimmer, Yunzhe Liu, Neža Vehar, Timothy E.J. Behrens, Raymond J. Dolan (20). memoryreplay. 10.18112/openneuro.ds002761.v1.1.2
25-participant MEG dataset — memoryreplay.
Quickstart#
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)
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'])
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.”
Cohort#
Dataset Statistics#
Channel counts: 306 ch (n=249 recordings)
Sampling frequencies: 600.0 Hz (n=249 recordings)
Signal · Electrodes & live trace#
No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.
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 |
memoryreplay |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
|
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS002761 · Wimmer2020eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS002761(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
memoryreplay
- Study:
ds002761(OpenNeuro)- Author (year):
Wimmer2020- Canonical:
—
Also importable as:
DS002761,Wimmer2020.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
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/ds002761 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002761 DOI: https://doi.org/10.18112/openneuro.ds002761.v1.1.2 NEMAR citation count: 1
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: 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/ds002761").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 ds002761 to reproduce the tutorial on this dataset.
Citation
G. Elliott Wimmer, Yunzhe Liu, Neža Vehar, Timothy E.J. Behrens, Raymond J. Dolan (20). memoryreplay. 10.18112/openneuro.ds002761.v1.1.2
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds002761.v1.1.2.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset