DS004324#

ToonFaces

Access recordings and metadata through EEGDash.

Citation: Luis Alberto Barradas Chacón, Selina C. Wriessnegger (2022). ToonFaces. 10.18112/openneuro.ds004324.v1.0.0

Modality: eeg Subjects: 26 Recordings: 161 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004324

dataset = DS004324(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 = DS004324(cache_dir="./data", subject="01")

Advanced query

dataset = DS004324(
    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{ds004324,
  title = {ToonFaces},
  author = {Luis Alberto Barradas Chacón and Selina C. Wriessnegger},
  doi = {10.18112/openneuro.ds004324.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004324.v1.0.0},
}

About This Dataset#

Images of stylized faces improve ERP features used for emotion detection

For their ease of accessibility and low cost, current Brain-Computer Interfaces (BCI) used to detect subjective emotional and affective states rely largely on electroencephalographic (EEG) signals. Numerous datasets are publicly available for any researcher to design models for affect detection from EEG. However, few designs focus on optimally exploiting the nature of the stimulus elicitation to improve accuracy. We found that artificially enhanced human faces with exaggerated visual features significantly improve some commonly used neural correlates of emotion as measured by event-related potentials (ERPs). These images elicit an enhanced N170 component, well known in facial recognition encoding. Our findings suggest that the study of emotion elicitation could exploit consistent stimuli transformations to study the characteristics of ERPs related to specific affective stimuli. Furthermore, this specific result might be useful in the context of affective BCI design, where a higher accuracy in affect detection from EEG can improve the experience of a user.

Participant information has been removed for annonimation reasons.

References

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896

Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8

Dataset Information#

Dataset ID	`DS004324`
Title	ToonFaces
Year	2022
Authors	Luis Alberto Barradas Chacón, Selina C. Wriessnegger
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004324.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: 26
Recordings: 161
Tasks: 1

Channels & sampling rate

Channels: 28 (26), 38 (26)
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS004324 class to access this dataset programmatically.

class eegdash.dataset.DS004324(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004324. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 26; recordings: 26; 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

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/ds004324 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004324

Examples

>>> from eegdash.dataset import DS004324
>>> dataset = DS004324(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