ON005028: eeg dataset, 11 subjects#
Comparing P300 Flashing paradigms in online typing with language models
Citation: Nand Chandravadia, Shrita Pendekanti, Dustin Roberts, Robert Tran, Saarang Panchavati, Corey Arnold, Nader Pouratian, William Speier (20). Comparing P300 Flashing paradigms in online typing with language models. 10.82901/nemar.on005028
11-participant EEG dataset — Comparing P300 Flashing paradigms in online typing with language models.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON005028
dataset = ON005028(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON005028(cache_dir="./data", subject="01")
Advanced query
dataset = ON005028(
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{on005028,
title = {Comparing P300 Flashing paradigms in online typing with language models},
author = {Nand Chandravadia and Shrita Pendekanti and Dustin Roberts and Robert Tran and Saarang Panchavati and Corey Arnold and Nader Pouratian and William Speier},
doi = {10.82901/nemar.on005028},
url = {https://doi.org/10.82901/nemar.on005028},
}
About This Dataset#
This dataset was created using BCI2000. The goal of this study was to explore the online typing performance of the P300 speller using language models and various flashing paradigms. For more information see Chandravadia et al. (https://www.medrxiv.org/content/10.1101/2022.06.24.22276882v1).
If you reference this dataset in your publications, please acknowledge its authors.
This dataset is made available under CC0.
Note: subject 5 was not included in the analysis because the testing stage did not include all three flashing paradigms.
Cohort#
Signal · Electrodes & live trace#
Live trace viewer — sub-01 · ses-test · run-1
Showing one representative recording out of
11 subjects and 105 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 |
Comparing P300 Flashing paradigms in online typing with language models |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Nand Chandravadia, Shrita Pendekanti, Dustin Roberts, Robert Tran, Saarang Panchavati, Corey Arnold, Nader Pouratian, William Speier |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on005028,
title = {Comparing P300 Flashing paradigms in online typing with language models},
author = {Nand Chandravadia and Shrita Pendekanti and Dustin Roberts and Robert Tran and Saarang Panchavati and Corey Arnold and Nader Pouratian and William Speier},
doi = {10.82901/nemar.on005028},
url = {https://doi.org/10.82901/nemar.on005028},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON005028(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Comparing P300 Flashing paradigms in online typing with language models
- Study:
on005028(NeMAR)- Author (year):
nan- Canonical:
—
Also importable as:
ON005028,nan.Modality:
eeg. Subjects: 11; recordings: 105; 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/on005028 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on005028 DOI: https://doi.org/10.82901/nemar.on005028
Examples
>>> from eegdash.dataset import ON005028 >>> dataset = ON005028(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 on005028 to reproduce the tutorial on this dataset.
Citation
Nand Chandravadia, Shrita Pendekanti, Dustin Roberts, Robert Tran, Saarang Panchavati, … (20). Comparing P300 Flashing paradigms in online typing with language models. 10.82901/nemar.on005028
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on005028.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset