DS005028#

Comparing P300 Flashing paradigms in online typing with language models

Access recordings and metadata through EEGDash.

Citation: Nand Chandravadia, Shrita Pendekanti, Dustin Roberts, Robert Tran, Saarang Panchavati, Corey Arnold, Nader Pouratian, William Speier (2024). Comparing P300 Flashing paradigms in online typing with language models. 10.18112/openneuro.ds005028.v1.0.0

Modality: eeg Subjects: 11 Recordings: 108 License: CC0 Source: openneuro Citations: 0.0

Metadata: Good (80%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005028

dataset = DS005028(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = DS005028(cache_dir="./data", subject="01")

Advanced query

dataset = DS005028(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds005028,
  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 = {10.18112/openneuro.ds005028.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005028.v1.0.0},
}

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.

Dataset Information#

Dataset ID	`DS005028`
Title	Comparing P300 Flashing paradigms in online typing with language models
Year	2024
Authors	Nand Chandravadia, Shrita Pendekanti, Dustin Roberts, Robert Tran, Saarang Panchavati, Corey Arnold, Nader Pouratian, William Speier
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005028.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 11
Recordings: 108
Tasks: —

Channels & sampling rate

Channels: Varies
Sampling rate (Hz): Varies
Duration (hours): 0.0

API Reference#

Use the DS005028 class to access this dataset programmatically.

class eegdash.dataset.DS005028(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005028. Modality: eeg; Experiment type: Motor. 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

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005028 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005028

Examples

>>> from eegdash.dataset import DS005028
>>> dataset = DS005028(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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None