DS005429: eeg dataset, 15 subjects#
Auditory oddball comparison (Optimum-1, Learning-oddball, and the local–global paradigm)
Citation: Renate Rutiku, Chiara Fiscone, Marcello Massimini, Simone Sarasso (2024). Auditory oddball comparison (Optimum-1, Learning-oddball, and the local–global paradigm). 10.18112/openneuro.ds005429.v1.0.0
15-participant EEG dataset — Auditory oddball comparison (Optimum-1, Learning-oddball, and the local–global paradigm).
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS005429
dataset = DS005429(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS005429(cache_dir="./data", subject="01")
Advanced query
dataset = DS005429(
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{ds005429,
title = {Auditory oddball comparison (Optimum-1, Learning-oddball, and the local–global paradigm)},
author = {Renate Rutiku and Chiara Fiscone and Marcello Massimini and Simone Sarasso},
doi = {10.18112/openneuro.ds005429.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds005429.v1.0.0},
}
About This Dataset#
This is the raw EEG data used in:
Rutiku, R., Fiscone, C., Massimini, M., & Sarasso, S. (2024). Assessing mismatch negativity (MMN) and P3b within‐individual sensitivity — A comparison between the local–global paradigm and two specialized oddball sequences. European Journal of Neuroscience, 59(5), 842-859.
Introduction
What’s in this dataset
Each participant (n=15) completed three different auditory oddball sequences: the Optimum-1 for MMN, the learning-oddball for P3b, and the local–global paradigm for the local and global effect. The tasks are formatted as different sessions but they were all recorded consecutively within one EEG experiment (order differed between participants). The local-global sequence was recorded in two separate EEG files (except for participant 5; see below for exception notes). Note that whereas the .vmrk files contain the original triggers for each recording, the _events files contain the correct event samples used in the analysis (in the fieldtrip cfg.trl format). It namely sometimes happened that some triggers were skipped by the recording system and these triggers needed to be interpolated using the event timestamps from the psychtoolbox output that was used to run the stimulation sequence (see below). Note also that the local-global sequence contains triggers for every single sound, but trials should be cut only for the first sound of every quintlet. The _events files already take that into account.
| Subject | Session | Run |
| ------- |--------------|-------|
| sub-01 | ses-MMN | |
| sub-01 | ses-P3b | |
| sub-01 | ses-LGeffect | run-1 |
| sub-01 | ses-LGeffect | run-2 |
Auditory stimulation specs
The stimulation sequence information is provided in the original .mat format in the sourcedata folder.
There are two files for each sequence: a file containing the sound definitions (_stimulation_SEQUENCE) and a file containing the timestamps for each sound (_critical_events). The code used to run these sequences is included in the paradigms folder.
Exceptions
Participant 13 was recorded with 5000 Hz EEG sampling rate whereas all other participants were recorded with 2500 Hz EEG sampling rate.
Participants 13, 14, and 15 were recorded chronologically first and they have slightly more trials for the oddball sequences. After inspecting their data, it was decided that trial numbers can be reduced for the rest of the participants in order to keep the recording time as short as possible while still having good sensitivity for the effects of interest. Participant 5 has three runs for the local-global task due to a need for an extra break by the participant.
Cohort#
Dataset Statistics#
Age distribution by gender (n=13, range 19–49 yr, mean 27.7 yr)
Sex composition
Channel counts: 64 ch (n=61 recordings)
Sampling frequencies (Hz)
Total recording duration: 14 h 23 min
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · ses-LGeffect · task-LocalGlobal · run-1
Showing one representative recording out of
15 subjects and 61 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.
Electrode layout — EEG · 62 sensors — 62 channels
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 |
Auditory oddball comparison (Optimum-1, Learning-oddball, and the local–global paradigm) |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2024 |
Authors |
Renate Rutiku, Chiara Fiscone, Marcello Massimini, Simone Sarasso |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds005429,
title = {Auditory oddball comparison (Optimum-1, Learning-oddball, and the local–global paradigm)},
author = {Renate Rutiku and Chiara Fiscone and Marcello Massimini and Simone Sarasso},
doi = {10.18112/openneuro.ds005429.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds005429.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005429 · Rutiku2024eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005429(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Auditory oddball comparison (Optimum-1, Learning-oddball, and the local–global paradigm)
- Study:
ds005429(OpenNeuro)- Author (year):
Rutiku2024- Canonical:
—
Also importable as:
DS005429,Rutiku2024.Modality:
eeg; Experiment type:Attention; Subject type:Healthy. Subjects: 15; recordings: 61; tasks: 3.- 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/ds005429 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005429 DOI: https://doi.org/10.18112/openneuro.ds005429.v1.0.0 NEMAR citation count: 0
Examples
>>> from eegdash.dataset import DS005429 >>> dataset = DS005429(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/ds005429").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 ds005429 to reproduce the tutorial on this dataset.
Citation
Renate Rutiku, Chiara Fiscone, Marcello Massimini, Simone Sarasso (2024). Auditory oddball comparison (Optimum-1, Learning-oddball, and the local–global paradigm). 10.18112/openneuro.ds005429.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.ds005429.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset