EEGDash/ds004951

Braille letters - EEG

Dataset ID: ds004951

Haupt2024_Braille

Canonical aliases: Haupt2025

At a glance: EEG · Tactile learning · other · 11 subjects · 23 recordings · CC0

Load this dataset

This repo is a pointer. The raw EEG data lives at its canonical source (OpenNeuro / NEMAR); EEGDash streams it on demand and returns a PyTorch / braindecode dataset.

# pip install eegdash
from eegdash import EEGDashDataset

ds = EEGDashDataset(dataset="ds004951", cache_dir="./cache")
print(len(ds), "recordings")

You can also load it by canonical alias — these are registered classes in eegdash.dataset:

from eegdash.dataset import Haupt2025
ds = Haupt2025(cache_dir="./cache")

If the dataset has been mirrored to the HF Hub in braindecode's Zarr layout, you can also pull it directly:

from braindecode.datasets import BaseConcatDataset
ds = BaseConcatDataset.pull_from_hub("EEGDash/ds004951")

Dataset metadata

Subjects	11
Age range	29–61 yrs, mean 44.2
Recordings	23
Tasks (count)	1
Sessions	2
Channels	64 (×13), 63 (×10)
Sampling rate (Hz)	1000 (×23)
Total duration (h)	25.9
Size on disk	22.0 GB
Recording type	EEG
Experimental modality	Tactile
Paradigm type	Learning
Population	Other
BIDS version	1.7.0
Source	openneuro
License	CC0
NEMAR citations	1

Tasks

• letters

Upstream README

Verbatim from the dataset's authors — the canonical description.

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

People

Authors

• Marleen Haupt
• Monika Graumann
• Santani Teng
• Carina Kaltenbach
• Radoslaw M. Cichy (senior)

Contact

• Marleen Haupt

Funding

• CI241/1-1
• CI241/3-1
• CI241/7-1
• ERC-StG-2018-803370

Links

• DOI: 10.18112/openneuro.ds004951.v1.0.0
• OpenNeuro: ds004951
• Browse 700+ datasets: EEGDash catalog
• Docs: https://eegdash.org
• Code: https://github.com/eegdash/EEGDash

Provenance

• Backend: s3 — s3://openneuro.org/ds004951
• Exact size: 23,627,351,784 bytes (22.0 GB)
• Ingested: 2026-04-06
• Stats computed: 2026-04-04

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Auto-generated from dataset_summary.csv and the EEGDash API. Do not edit this file by hand — update the upstream source and re-run scripts/push_metadata_stubs.py.