DS004951: eeg dataset, 11 subjects#
Braille letters - EEG
Citation: Marleen Haupt, Monika Graumann, Santani Teng, Carina Kaltenbach, Radoslaw M. Cichy (2019). Braille letters - EEG. 10.18112/openneuro.ds004951.v1.0.0
11-participant EEG dataset — Braille letters - EEG.
Quickstart#
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)
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'])
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
Cohort#
Dataset Statistics#
Age distribution by gender (n=11, range 29–61 yr, mean 44.2 yr)
Sex composition
Channel counts (ch)
Sampling frequencies: 1000.0 Hz (n=23 recordings)
Total recording duration: 25 h
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · ses-02 · task-letters · run-01
Showing one representative recording out of
11 subjects and 23 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.
Electrode layout — EEG · 63 sensors — 63 channels
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 |
Braille letters - EEG |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2019 |
Authors |
Marleen Haupt, Monika Graumann, Santani Teng, Carina Kaltenbach, Radoslaw M. Cichy |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004951 · Haupt2024_Brailleeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004951(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Braille letters - EEG
- Study:
ds004951(OpenNeuro)- Author (year):
Haupt2024_Braille- Canonical:
—
Also importable as:
DS004951,Haupt2024_Braille.Modality:
eeg; Experiment type:Learning; Subject type:Other. 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
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/ds004951 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004951 DOI: https://doi.org/10.18112/openneuro.ds004951.v1.0.0 NEMAR citation count: 1
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: 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.pytorchdatasets.load_dataset("EEGDash/ds004951").huggingface
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 ds004951 to reproduce the tutorial on this dataset.
Citation
Marleen Haupt, Monika Graumann, Santani Teng, Carina Kaltenbach, Radoslaw M. Cichy (2019). Braille letters - EEG. 10.18112/openneuro.ds004951.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004951.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset