ON003190: eeg dataset, 19 subjects#
Assesment of the visual stimuli properties in P300 paradigm
Citation: Omar Mendoza-Montoya, Javier M. Antelis (—). Assesment of the visual stimuli properties in P300 paradigm. 10.82901/nemar.on003190
19-participant EEG dataset — Assesment of the visual stimuli properties in P300 paradigm.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON003190
dataset = ON003190(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON003190(cache_dir="./data", subject="01")
Advanced query
dataset = ON003190(
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{on003190,
title = {Assesment of the visual stimuli properties in P300 paradigm},
author = {Omar Mendoza-Montoya and Javier M. Antelis},
doi = {10.82901/nemar.on003190},
url = {https://doi.org/10.82901/nemar.on003190},
}
About This Dataset#
Dataset description:
The database consists of a total of 382 electroencephalographic files from 19 participants. All recordings were collected on channels Fz, Cz, P3, Pz,P4, PO7, PO8 and Oz, according to the 10-20 EEG electrode placement standard, grounded to AFz channel and referenced to right mastoid (M2).
Each participant (S1-S19) performed 3 experimental sessions (Session01-Session03) and in each session there are 7 data files.
The filenames for these data files are ’Training 4’, ’Training 5 - SF’, ’Training 5 - CF’, ’Training 6’, ’Training 7’, ’Training 8’, and ’Training 9’.
The number accompanying the filename indicates the number of stimuli, whereas letters SF and CF for data files with 5 stimuli indicate the type of flash, SF for Standard-Flash of the stimulus and CF for superimposing a yellow smiling Cartoon Face.
Note that filenames for data-files with 4, 6, 7, 8, and 9 stimuli do not have a letter and were recorded with the type of flash that provided the greater classification accuracy when using 5 stimuli.
Each data file contains the data stream in a 2D matrix where rows correspond to channels and columns correspond to time samples with sampling frequency of 256Hz.
There are 10 rows, 1 to 8 for each EEG electrode (in descending order Fz, Cz, P3, Pz, P4, PO7, PO8 and Oz), 9 for time stamps, and 10 for a marker that encode information about the execution of theexperiment.
The marker encodes this information as follows: - (i)marker numbers 101, 200, 201, 202 and 203, indicate the beginning and end of the five phases in a block - (ii)marker numbers 1, 2, 3, 4, 5, 6, 7, 8 and 9, indicate the symbol that is activated on the screen - (iii)each phase of the experiment block is identified with a marker - (iv)the phases of one block of the experiment are: Fixation, Target Presentation, Preparation, Stimulation and Rest - (iv)in particular the Stimulation phase has a start marker and an end marker
Cohort#
Dataset Statistics#
Channel counts (ch)
Sampling frequencies: 256.0 Hz (n=384 recordings)
Total recording duration: 39 h
Signal · Electrodes & live trace#
Live trace viewer — sub-01 · ses-01 · task-cnos · run-4
Showing one representative recording out of
19 subjects and 384 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 |
Assesment of the visual stimuli properties in P300 paradigm |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Omar Mendoza-Montoya, Javier M. Antelis |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on003190,
title = {Assesment of the visual stimuli properties in P300 paradigm},
author = {Omar Mendoza-Montoya and Javier M. Antelis},
doi = {10.82901/nemar.on003190},
url = {https://doi.org/10.82901/nemar.on003190},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON003190(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Assesment of the visual stimuli properties in P300 paradigm
- Study:
on003190(NeMAR)- Author (year):
nan- Canonical:
—
Also importable as:
ON003190,nan.Modality:
eeg. Subjects: 19; recordings: 384; tasks: 2.- 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/on003190 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on003190 DOI: https://doi.org/10.82901/nemar.on003190
Examples
>>> from eegdash.dataset import ON003190 >>> dataset = ON003190(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 on003190 to reproduce the tutorial on this dataset.
Citation
Omar Mendoza-Montoya, Javier M. Antelis (n.d.). Assesment of the visual stimuli properties in P300 paradigm. 10.82901/nemar.on003190
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on003190.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset