DS004951#

Braille letters - EEG

Access recordings and metadata through EEGDash.

Citation: Marleen Haupt, Monika Graumann, Santani Teng, Carina Kaltenbach, Radoslaw M. Cichy (2024). Braille letters - EEG. 10.18112/openneuro.ds004951.v1.0.0

Modality: eeg Subjects: 11 Recordings: 200 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004951

dataset = DS004951(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 = DS004951(cache_dir="./data", subject="01")

Advanced query

dataset = DS004951(
    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{ds004951,
  title = {Braille letters - EEG},
  author = {Marleen Haupt and Monika Graumann and Santani Teng and Carina Kaltenbach and Radoslaw M. Cichy},
  doi = {10.18112/openneuro.ds004951.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004951.v1.0.0},
}

About This Dataset#

This dataset contains the raw EEG data accompanying the paper “The transformation of sensory to perceptual braille letter representations in the visually deprived brain”. Please cite the above paper if you use this data.

The dataset includes:

Brainvision files (.eeg, .vhdr, .vmrk) for all participants.

Please note, for some participants the EEG decording had to be stopped and restarted within a session. In this case, the different files are indicated as separate runs. In addition, some participants completed a second session.

The events files contain the onsets, durations, trial types and values for all trials in the corresponding run. Stimuli are Braille letters (B,C,D,L,M,N,V,Z) presented on Braille cells under the left and right index fingers of participants. Triggers S1-8 are letters presented to the left hand, triggers S9-16 are letters presented to the right hand.

Other triggers:

starttrigger = S100; trialonset = S101; stimulusonset = S222; catchtrial = S200; pedalpress_correct = S253; pedalpress_incorrect = S254; endtrigger = S255;

For a full description of the paradigm and the employed procedures please see the paper.

References for MNE BIDS conversion

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	`DS004951`
Title	Braille letters - EEG
Year	2024
Authors	Marleen Haupt, Monika Graumann, Santani Teng, Carina Kaltenbach, Radoslaw M. Cichy
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004951.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: 200
Tasks: 1

Channels & sampling rate

Channels: 63 (33), 64 (13)
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS004951 class to access this dataset programmatically.

class eegdash.dataset.DS004951(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004951. Modality: eeg; Experiment type: Learning. Subjects: 11; recordings: 23; 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/ds004951 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004951

Examples

>>> from eegdash.dataset import DS004951
>>> dataset = DS004951(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