DS003061: eeg dataset, 13 subjects#
EEG data from an auditory oddball task
Citation: Arnaud Delorme (—). EEG data from an auditory oddball task. 10.18112/openneuro.ds003061.v1.1.0
13-participant EEG dataset — EEG data from an auditory oddball task.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS003061
dataset = DS003061(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS003061(cache_dir="./data", subject="01")
Advanced query
dataset = DS003061(
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{ds003061,
title = {EEG data from an auditory oddball task},
author = {Arnaud Delorme},
doi = {10.18112/openneuro.ds003061.v1.1.0},
url = {https://doi.org/10.18112/openneuro.ds003061.v1.1.0},
}
About This Dataset#
Data collection took place at the Meditation Research Institute (MRI) in Rishikesh, India under the supervision of Arnaud Delorme, PhD. The project was approved by the local MRI Indian ethical committee and the ethical committee of the University of California San Diego (IRB project # 090731).
Participants sat either on a blanket on the floor or on a chair for both experimental periods depending on their personal preference. They were asked to keep their eyes closed and all lighting in the room was turned off during data collection. An intercom allowed communication between the experimental and the recording room.
Participants performed three identical sessions of 13 minutes each. 750 stimuli were presented with 70% of them being standard (500 Hz pure tone lasting 60 milliseconds), 15% being oddball (1000 Hz pure tone lasting 60 ms) and 15% being distractors (1000 Hz white noise lasting 60 ms). All sounds took 5 milliseconds to ramp up and 5 milliseconds to ramp down. Sounds were presented at a rate of 1 per second with a random gaussian jitter of standard deviation 25 ms. Participants were instructed to respond to oddball by pressing a key on a keypad that was resting on their lap.
Cohort#
Dataset Statistics#
Age distribution by gender (n=13, range 24–58 yr, mean 34.7 yr)
Sex composition
Channel counts: 79 ch (n=39 recordings)
Sampling frequencies: 256.0 Hz (n=39 recordings)
Total recording duration: 8 h 12 min
Signal · Electrodes & live trace#
Live trace viewer — sub-010 · task-P300 · run-1
Showing one representative recording out of
13 subjects and 39 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 · 64 sensors — 64 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 |
EEG data from an auditory oddball task |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Arnaud Delorme |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds003061,
title = {EEG data from an auditory oddball task},
author = {Arnaud Delorme},
doi = {10.18112/openneuro.ds003061.v1.1.0},
url = {https://doi.org/10.18112/openneuro.ds003061.v1.1.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003061 · Delorme2020_auditory_oddballeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003061(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
EEG data from an auditory oddball task
- Study:
ds003061(OpenNeuro)- Author (year):
Delorme2020_auditory_oddball- Canonical:
—
Also importable as:
DS003061,Delorme2020_auditory_oddball.Modality:
eeg; Experiment type:Attention; Subject type:Healthy. Subjects: 13; recordings: 39; 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/ds003061 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003061 DOI: https://doi.org/10.18112/openneuro.ds003061.v1.1.0 NEMAR citation count: 4
Examples
>>> from eegdash.dataset import DS003061 >>> dataset = DS003061(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/ds003061").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 ds003061 to reproduce the tutorial on this dataset.
Citation
Arnaud Delorme (n.d.). EEG data from an auditory oddball task. 10.18112/openneuro.ds003061.v1.1.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds003061.v1.1.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset