---
pretty_name: "Braille letters - EEG"
license: cc0-1.0
tags:
  - eeg
  - neuroscience
  - eegdash
  - brain-computer-interface
  - pytorch
  - tactile
  - learning
  - other
  - letters
size_categories:
  - n<1K
task_categories:
  - other
authors:
  - "Marleen Haupt"
  - "Monika Graumann"
  - "Santani Teng"
  - "Carina Kaltenbach"
  - "Radoslaw M. Cichy"
---

# 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](https://github.com/eegdash/EEGDash) streams it
on demand and returns a PyTorch / braindecode dataset.

```python
# 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`:

```python
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:

```python
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](https://doi.org/10.18112/openneuro.ds004951.v1.0.0)
- **OpenNeuro:** [ds004951](https://openneuro.org/datasets/ds004951)
- **Browse 700+ datasets:** [EEGDash catalog](https://huggingface.co/spaces/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

---

_Auto-generated from [dataset_summary.csv](https://github.com/eegdash/EEGDash/blob/main/eegdash/dataset/dataset_summary.csv) and the [EEGDash API](https://data.eegdash.org/api/eegdash/datasets/summary/ds004951). Do not edit this file by hand — update the upstream source and re-run `scripts/push_metadata_stubs.py`._