ON004368: eeg dataset, 39 subjects#
Meta-rdk: Preprocessed EEG data
Citation: Martin Rouy, Matthieu Roger, Dorian Goueytes, Michael Pereira, Paul Roux, Nathan Faivre (—). Meta-rdk: Preprocessed EEG data. 10.82901/nemar.on004368
39-participant EEG dataset — Meta-rdk: Preprocessed EEG data.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON004368
dataset = ON004368(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON004368(cache_dir="./data", subject="01")
Advanced query
dataset = ON004368(
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{on004368,
title = {Meta-rdk: Preprocessed EEG data},
author = {Martin Rouy and Matthieu Roger and Dorian Goueytes and Michael Pereira and Paul Roux and Nathan Faivre},
doi = {10.82901/nemar.on004368},
url = {https://doi.org/10.82901/nemar.on004368},
}
About This Dataset#
The study was approved by the ethical committee Sud Méditérannée II (217 R01). Twenty individuals with a schizophrenia spectrum disorder (schizophrenia or schizoaffective disorder, 16 males, 4 females) and 22 healthy participants (15 males, 7 females) from the general population took part in this study. Schizophrenia and schizoaffective disorders were diagnosed based on the Structured Clinical Interview for assessing the DSM-5 criteria. The control group was screened for current or past psychiatric illness, and individuals were excluded if they met the criteria for a severe and persistent mental disorder.
We used a visual discrimination task. Stimuli consisted of 100 moving dots within a circle (3° radius) at the center of the screen. On each trial, participants indicated whether the motion direction of the dots was to the left or to the right by reaching and clicking on one of two choice targets (3° radius circle) at the top corners of the screen with a mouse. After 6 seconds without response, a buzz sound rang and a message was displayed inviting the participant to respond quicker. Motion coherence was adapted at the individual level via a 1up/2down staircase procedure in order to match task-performance between groups. Following each perceptual decision, participants were asked to report their confidence about their response using a vertical visual analog scale from 0% (Sure incorrect) to 100% (Sure correct), with 50% confidence meaning “Not sure at all”.
Cohort#
Dataset Statistics#
Age distribution (n=39, range 19–62 yr, mean 41.2 yr · sex per subject not reported)
Sex composition
Channel counts: 63 ch (n=40 recordings)
Sampling frequencies: 128.0 Hz (n=40 recordings)
Total recording duration: 8 h 27 min
Signal · Electrodes & live trace#
Live trace viewer — sub-S01 · ses-1 · task-task
Showing one representative recording out of
39 subjects and 40 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
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 |
Meta-rdk: Preprocessed EEG data |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Martin Rouy, Matthieu Roger, Dorian Goueytes, Michael Pereira, Paul Roux, Nathan Faivre |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on004368,
title = {Meta-rdk: Preprocessed EEG data},
author = {Martin Rouy and Matthieu Roger and Dorian Goueytes and Michael Pereira and Paul Roux and Nathan Faivre},
doi = {10.82901/nemar.on004368},
url = {https://doi.org/10.82901/nemar.on004368},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON004368(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Meta-rdk: Preprocessed EEG data
- Study:
on004368(NeMAR)- Author (year):
nan- Canonical:
—
Also importable as:
ON004368,nan.Modality:
eeg. Subjects: 39; recordings: 40; 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/on004368 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on004368 DOI: https://doi.org/10.82901/nemar.on004368
Examples
>>> from eegdash.dataset import ON004368 >>> dataset = ON004368(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.pytorch
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 on004368 to reproduce the tutorial on this dataset.
Citation
Martin Rouy, Matthieu Roger, Dorian Goueytes, Michael Pereira, Paul Roux, … (n.d.). Meta-rdk: Preprocessed EEG data. 10.82901/nemar.on004368
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on004368.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset