DS006253: ieeg dataset, 23 subjects#
MetaRDK
Citation: Dorian Goueytes, Francois Stockart, Alexis Robin, Lucien Gyger, Martin Rouy, Dominique Hoffmann, Lorella Minotti, Philippe Kahane, Michael Pereira, Nathan Faivre (—). MetaRDK. 10.18112/openneuro.ds006253.v1.0.3
23-participant iEEG dataset — MetaRDK.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS006253
dataset = DS006253(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS006253(cache_dir="./data", subject="01")
Advanced query
dataset = DS006253(
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{ds006253,
title = {MetaRDK},
author = {Dorian Goueytes and Francois Stockart and Alexis Robin and Lucien Gyger and Martin Rouy and Dominique Hoffmann and Lorella Minotti and Philippe Kahane and Michael Pereira and Nathan Faivre},
doi = {10.18112/openneuro.ds006253.v1.0.3},
url = {https://doi.org/10.18112/openneuro.ds006253.v1.0.3},
}
About This Dataset#
Goueytes, D., Gigyer, L., Rouy, M., Hoffmann, D., Minotti, L., Kahane, P., Pereira, M., and Faivre, N. Evidence accumulation in the pre-supplementary motor area and insula drives confidence and changes of mind.
MetaRDK is a project aiming to understand the neural correlates of decision making and decision making-related metacognition. Epileptic patient with pharmacology intractable epilepsy performed a perceptual decision making task associated with a confidence judgement task.
Evidence accumulation in the pre-supplementary motor area and insula drives confidence and changes of mind
The patients had to decide within a window of 6s if a a cloud of dot displayed at the center of the screen was moving right or left, and provide their answer by moving a computer mouse and clicking on the corresponding right/left target. The difficulty of the task was titrated for all patients at 70% correct using an adaptive staircase. After each answer patients were prompted to evaluate how confidence they felt in their decision on a scale from 0 to 100 (0 : Sure to be wrong, 50 : answered at random, 100 : Sure to be right
All scripts for the task, data processing and analysis are available here : doi: 10.17605/OSF.IO/2KT97
Description of the contents of the dataset
View full README
Evidence accumulation in the pre-supplementary motor area and insula drives confidence and changes of mind
The patients had to decide within a window of 6s if a a cloud of dot displayed at the center of the screen was moving right or left, and provide their answer by moving a computer mouse and clicking on the corresponding right/left target. The difficulty of the task was titrated for all patients at 70% correct using an adaptive staircase. After each answer patients were prompted to evaluate how confidence they felt in their decision on a scale from 0 to 100 (0 : Sure to be wrong, 50 : answered at random, 100 : Sure to be right
All scripts for the task, data processing and analysis are available here : doi: 10.17605/OSF.IO/2KT97
Description of the contents of the dataset
This dataset contains the high-gamma content (average power modulation in five non-overlapping frequency bands between 70 and 150Hz) of the patient while they performed the task. The data are segmented, and each segment contain high-gamma activity from trial onset (clicking on the start button) to trial offset (following the confidence judgement response), sampled at 512Hz.
All information regarding the behavior of the patients (stimulus onset, response time, confidence judgements) are available in the derivative/beh directory as a separated .csv table for each patient.
The data provided were screened in order to remove trials and iEEG channels with high epilepsy-related artifact.
Methods
Subjects
All participants were patients with pharmacologically intractable epilepsy.
Apparatus
Recordings were performed at the bedside of the patients using a micromed recording system. The implantation schema was decided by the medical team solely based on the medical status of the patients.
Initial setup
The patient sat reclined in their hospital bed, with a laptop and a mouse in front of them. The task was explained to them, and they were instructed to sample the stimuli as long as required within 6s to form their decision. The confidence judgement scale was explained, and they were explicitly instructed to use the whole confidence scale.
Task organization
The patients first perform a short initial staircase session to titrate difficulty at 70% (the staircase procedure was maintained during the task
The staircase was followed by the main task, corresponding to the data shared in this dataset.
Task details
Each trial was initiated by clicking on a ‘start’ button at the bottom of the screen. This click corresponds to the trial onset. After a fixed delay, the stimulus was presented (stimonset). The decision was recorded as soon as the participants started to move the mouse (decision time), and the response was recorded upon clicking on the target button (response/R1). The confidence scale was then displayed after a fixed delay (VAS onset), and the click on the confidence scale was also recorded (R2). After a 500 ms delay, the trial ended (trial offset). For each trial, we also recorded outcome (correct), the presence of change of mind (ch_mind) and their timing (rt_chmind), as well as the coherence of the stimulus (stim_int) and the max velocity of the computer mouse (vmax). The identity and timing of all this elements is available in the derivates/beh directory
Cohort#
Dataset Statistics#
Channel counts (ch)
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 |
MetaRDK |
Author (year) |
|
Canonical |
|
Importable as |
|
Year |
— |
Authors |
Dorian Goueytes, Francois Stockart, Alexis Robin, Lucien Gyger, Martin Rouy, Dominique Hoffmann, Lorella Minotti, Philippe Kahane, Michael Pereira, Nathan Faivre |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds006253,
title = {MetaRDK},
author = {Dorian Goueytes and Francois Stockart and Alexis Robin and Lucien Gyger and Martin Rouy and Dominique Hoffmann and Lorella Minotti and Philippe Kahane and Michael Pereira and Nathan Faivre},
doi = {10.18112/openneuro.ds006253.v1.0.3},
url = {https://doi.org/10.18112/openneuro.ds006253.v1.0.3},
}
API Reference#
eegdash.datasetEEGDashDatasetMetaRDKDS006253 · Goueytes2024 · MetaRDKeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS006253(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
MetaRDK
- Study:
ds006253(OpenNeuro)- Author (year):
Goueytes2024- Canonical:
MetaRDK
Also importable as:
DS006253,Goueytes2024,MetaRDK.Modality:
ieeg; Experiment type:Decision-making; Subject type:Epilepsy. Subjects: 23; recordings: 201; tasks: 4.- 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/ds006253 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006253 DOI: https://doi.org/10.18112/openneuro.ds006253.v1.0.3
Examples
>>> from eegdash.dataset import DS006253 >>> dataset = DS006253(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/ds006253").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 ds006253 to reproduce the tutorial on this dataset.
Citation
Dorian Goueytes, Francois Stockart, Alexis Robin, Lucien Gyger, Martin Rouy, … (n.d.). MetaRDK. 10.18112/openneuro.ds006253.v1.0.3
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds006253.v1.0.3.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset