DS005065: meg dataset, 21 subjects#
Heuristics in risky decision-making relate to preferential representation of information MEG data
Citation: Evan M. Russek, Rani Moran, Yunzhe Liu, Ray Dolan, Quentin Huys (—). Heuristics in risky decision-making relate to preferential representation of information MEG data. 10.18112/openneuro.ds005065.v1.0.0
21-participant MEG dataset — Heuristics in risky decision-making relate to preferential representation of information MEG data.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS005065
dataset = DS005065(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS005065(cache_dir="./data", subject="01")
Advanced query
dataset = DS005065(
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{ds005065,
title = {Heuristics in risky decision-making relate to preferential representation of information MEG data},
author = {Evan M. Russek and Rani Moran and Yunzhe Liu and Ray Dolan and Quentin Huys},
doi = {10.18112/openneuro.ds005065.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds005065.v1.0.0},
}
About This Dataset#
The task consisted of 13 scanner runs (except for subject 1 who completed 5 rather than 3 localizer runs). Runs 1-3 (1-5 for subject 1) are the localizer task. Runs 4-5 are non-analyzed data from the ‘probability learning’ task. Runs 6-13 (8-15 for subject 1) are the risky decision-making task.
Event times were recorded with a photodiode, which is accessible as a MEG channel. This has been processed so that event times are listed in derivatives/Event_Info_Tables. Raw times of events in the scan are in column “onset_time”. The corresponding index into the unprocessed MEG data is in column “scanner_onset_idx”. The onset into the downsampled data is in “onset_idx_ds”. In the table, each row corresponds to an event. Block number denotes which scanner run that event belongs to. For the localizer task (denoted in phase column), events are image onsets. “image_type” specifies the role of that image in the task (“CHOICE” or “OUTCOME”) and “image_number” denotes which choice or outcome it is (see paper Fig. 1). Finally, “image_name” denotes which image category was shown (e.g. “Hand”). For the task, events correspond to gamble information onset (Info), Probability stimulus presentation (“Choice”), response (“Gamble Response”) and outcome onset (“Outcome”). Columns denote which image was shown and what the response was (accept).
derivatives/Epoched_Data contains epoched preprocessed data for each subject for the localizer task and then around each choice in the main choice task. Both are epoched from from 0-500 ms following the event.
Code to analyze the data along with additional behavioral data is available at evanrussek/MEG_Heuristics_Risk_Preferential_Information
Cohort#
Dataset Statistics#
Channel counts (ch)
Sampling frequencies: 1200.0 Hz (n=272 recordings)
Total recording duration: 68 h
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 |
Heuristics in risky decision-making relate to preferential representation of information MEG data |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Evan M. Russek, Rani Moran, Yunzhe Liu, Ray Dolan, Quentin Huys |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds005065,
title = {Heuristics in risky decision-making relate to preferential representation of information MEG data},
author = {Evan M. Russek and Rani Moran and Yunzhe Liu and Ray Dolan and Quentin Huys},
doi = {10.18112/openneuro.ds005065.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds005065.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005065 · Russek2024eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005065(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Heuristics in risky decision-making relate to preferential representation of information MEG data
- Study:
ds005065(OpenNeuro)- Author (year):
Russek2024- Canonical:
—
Also importable as:
DS005065,Russek2024.Modality:
meg; Experiment type:Decision-making; Subject type:Healthy. Subjects: 21; recordings: 275; 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/ds005065 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005065 DOI: https://doi.org/10.18112/openneuro.ds005065.v1.0.0 NEMAR citation count: 1
Examples
>>> from eegdash.dataset import DS005065 >>> dataset = DS005065(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/ds005065").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 ds005065 to reproduce the tutorial on this dataset.
Citation
Evan M. Russek, Rani Moran, Yunzhe Liu, Ray Dolan, Quentin Huys (n.d.). Heuristics in risky decision-making relate to preferential representation of information MEG data. 10.18112/openneuro.ds005065.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.ds005065.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset