ON007114: eeg dataset, 28 subjects#
SUBEMOcondEEG1
Access recordings and metadata through EEGDash.
Citation: Martina T. Cinca-Tomás, Judith Domínguez-Borràs (2024). SUBEMOcondEEG1. 10.82901/nemar.on007114
Modality: eeg Subjects: 28 Recordings: 28 License: CC0 Source: nemar
Metadata: Complete (100%)
28-participant EEG dataset — SUBEMOcondEEG1.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON007114
dataset = ON007114(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON007114(cache_dir="./data", subject="01")
Advanced query
dataset = ON007114(
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{on007114,
title = {SUBEMOcondEEG1},
author = {Martina T. Cinca-Tomás and Judith Domínguez-Borràs},
doi = {10.82901/nemar.on007114},
url = {https://doi.org/10.82901/nemar.on007114},
}
About This Dataset#
Task:
Participants were instructed to respond, as quickly and accurately as possible, on which side they heard a voice (left or right, by pressing a keyboard button with their right index or middle finger, respectively), while maintaining their gaze on a permanent fixation cross at the center of the screen.
The task consisted of a fear conditioning paradigm with a Pre-Conditioning, a Conditioning and an Extinction phase. During Conditioning, participants were presented with both highAM and lowAM voices, and these were either paired (CS+) or unpaired (CS-) with the unpleasant loud white noise (US). Stimulus assignment as either CS+ or CS- was based on the gender of each voice and counterbalanced across participants (i.e., 50% were presented with highAM and lowAM CS+ male voices, and with highAM and lowAM CS- female voices). During Pre-Conditioning, the same CS stimuli were presented without the US. The fear conditioning was conducted with a 50% partial reinforcement. Thirty-two healthy volunteers were mostly recruited among University of Barcelona students. All were right-handed, with normal hearing and no previous neurological or psychiatric disorders. Four participants were excluded from the analysis due to low signal-to-noise ratio on the electrophysiological data. Thus, the final sample consisted of twenty-eight participants (12 females; age range 18-31, mean age = 22 years). All procedures were approved by the ethics committee of the University of Barcelona (RB00003099 - CER042405), in accordance with the Declaration of Helsinki (2024). All volunteers provided written informed consent and received monetary compensation for their participation. EEG, pupillometry and behavioral data are stored separately in corresponding folders: beh - behavioral data: Contingency, Valence, Arousal, Response Time, Hit Rate eeg - EEG data pupil - pupillometry
Cohort#
Dataset Statistics#
Age distribution (n=28, range 18–31 yr, mean 22.2 yr · sex per subject not reported)
Sex composition
Channel counts: 68 ch (n=28 recordings)
Sampling frequencies: 500.0 Hz (n=28 recordings)
Total recording duration: 40 h
Signal · Electrodes & live trace#
Live trace viewer — sub-17 · task-SUBEMOcondEEG1
Showing one representative recording out of
28 subjects and 28 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 |
SUBEMOcondEEG1 |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
2024 |
Authors |
Martina T. Cinca-Tomás, Judith Domínguez-Borràs |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on007114,
title = {SUBEMOcondEEG1},
author = {Martina T. Cinca-Tomás and Judith Domínguez-Borràs},
doi = {10.82901/nemar.on007114},
url = {https://doi.org/10.82901/nemar.on007114},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON007114(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
SUBEMOcondEEG1
- Study:
on007114(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
ON007114.Modality:
eeg; Subject type:Unknown. Subjects: 28; recordings: 28; 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/on007114 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on007114 DOI: https://doi.org/10.82901/nemar.on007114
Examples
>>> from eegdash.dataset import ON007114 >>> dataset = ON007114(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 on007114 to reproduce the tutorial on this dataset.
Citation
Martina T. Cinca-Tomás, Judith Domínguez-Borràs (2024). SUBEMOcondEEG1. 10.82901/nemar.on007114
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on007114.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset