---
pretty_name: "DECOMEG: Brain Activity During Typing (MEG & EEG)"
license: cc-by-nc-4.0   
language:
  - es
tags:
  - neuroscience
  - meg
  - eeg
  - brain-computer-interface
  - bci
  - brain-to-text
  - typing
  - motor
  - electrophysiology
task_categories:
  - other
size_categories:
  - 100GB<n<1TB
modalities:
  - timeseries
---

# DECOMEG — Brain Activity During Typing (MEG & EEG)

Non-invasive brain recordings (magnetoencephalography, MEG; and electroencephalography, EEG)
of healthy adults typing briefly-memorized sentences on a QWERTY keyboard. This is the dataset
underlying **Brain2Qwerty** (Lévy et al., 2025) and its companion neuroscience study
(Zhang et al., 2025).

## Summary

- **Participants:** 35 healthy adult volunteers recruited at the Basque Center on Cognition,
  Brain and Language (BCBL), San Sebastián, Spain. All native Spanish speakers, right-handed,
  and skilled typists (selected for typing accuracy ≥ 80%). Cohort: 23% men / 77% women,
  mean age 31.6 ± 5.2 years. Five participants took part in **both** EEG and MEG sessions.
- **Task:** Each trial had three phases — **read → wait → type**. A Spanish sentence was shown
  word-by-word (rapid serial visual presentation, RSVP); after the last word a fixation cross
  appeared for 1.5 s; its disappearance cued the participant to type the sentence from memory
  **without any on-screen feedback**. Each session used 128 unique declarative Spanish sentences
  of 5–8 words.
- **Languages / stimuli:** Spanish sentences. MEG: ~5.1K sentences / ~193K characters.
  EEG: ~4K sentences / ~146K characters.
- **Keyboard:** A custom MR-compatible QWERTY keyboard (HybridMojo LLC) with non-ferromagnetic
  silver-spring key mechanisms, to avoid magnetic artifacts in the MEG.

## Recording devices

| Modality | System | Channels | Sampling rate | Online filters |
|---|---|---|---|---|
| MEG | Megin (Elekta Neuromag) | 306 (102 magnetometers + 204 planar gradiometers) | 1 kHz | 0.1 Hz high-pass, 330 Hz low-pass |
| EEG | BrainVision actiCAP slim | 64 | 1 kHz | — |

Per-participant recording time: EEG 0.88 ± 0.02 h, MEG 0.93 ± 0.01 h (≈17.7 h EEG and
≈21.5 h MEG of typing in total).

## Directory structure

```
pinet2024_public/
├── MEG/
│   ├── FIF/      # raw continuous MEG (Elekta/Megin .fif), one directory per recording;
│   │             #   each holds the typing blocks (block1.fif, block2.fif) + a tapping localizer
│   └── logs/     # behavioral logs (MATLAB .mat): stimuli, keystrokes, and timing
└── EEG/
    ├── EEG/      # raw EEG in BrainVision format (.eeg / .vhdr / .vmrk)
    └── logs/     # behavioral logs (MATLAB .mat): stimuli, keystrokes, and timing

```

### File counts (this release)

| | Files |
|---|---|
| MEG raw `.fif` | 231 (across 29 recording directories) |
| MEG behavioral logs `.mat` | 84 |
| EEG recordings (`.eeg`/`.vhdr`/`.vmrk` triplets) | 117 each |
| EEG behavioral logs `.mat` | 62 |
| Total size | ≈ 262 GB |

### Repeated participants (MEG)

Some people took part in more than one MEG session and appear under **different subject IDs**.
The following IDs belong to the **same person**:

| Person | Subject IDs |
|---|---|
| 1 | `S1`, `S18` |
| 4 | `S4`, `S14` |
| 5 | `S5`, `S10`, `S21` |

Merging these (and excluding `S23`, who had a metallic implant) yields **19 unique MEG
participants**. The Brain2Qwerty V1 pipeline applies exactly this mapping in its
`SpanishBCBLPreprocessing` event transform.

## File formats

- **`.fif`** — Elekta/Megin/MNE raw MEG.
- **`.vhdr` / `.eeg` / `.vmrk`** — BrainVision EEG (header / data / markers).
- **`.mat`** — MATLAB behavioral logs (stimuli, keystrokes, timing). Load with
  `scipy.io.loadmat` or MATLAB.

## Loading the events (keystroke / word / sentence timings)

The [Brain2Qwerty code release](https://github.com/facebookresearch/brain2qwerty) ships a
`studies` package that reads these recordings and behavioural logs, aligns them, and emits a
standardized **event** dataframe. Install the public libraries and let it download and build
the events for you:

```bash
pip install neuralset neuralfetch
```

Clone the [Brain2Qwerty repo](https://github.com/facebookresearch/brain2qwerty) and run the
snippet from its `brain2qwerty/` directory (or `pip install -e .` there first) so that
`import studies` resolves — `studies` is the package that defines and registers `Pinet2024Meg` /
`Pinet2024Eeg`; it is not part of `neuralfetch`.

```python
import studies  # noqa: F401  - registers Pinet2024Meg / Pinet2024Eeg
from neuralset.events import Study

study = Study(name="Pinet2024Meg", path="SpanishBCBL")  # use "Pinet2024Eeg" for EEG
study.download()        # fetch this study's recordings + logs from this HF repo into `path`
events = study.build()  # standardized event dataframe across all subjects/sessions
```

`events` has one row per event, with `type` (`Keystroke` / `Word` / `Sentence`, plus the raw
`Meg`/`Eeg` recording rows) and the timings in `start` (onset, seconds) and `duration`
(seconds). If you have already downloaded the dataset, point `path` at the local folder and
call `study.build()` directly (skip `download()`).

## Ethics & privacy

Recordings are from consenting healthy adult volunteers under the study's approved ethics
protocol at BCBL. Directly identifying material (structural MRI/T1, head-position videos,
eye-tracking, and session videos) present in the internal dataset has been **excluded** from
this public release; only de-identified M/EEG recordings and behavioral logs are included.

## License

Released under **CC BY-NC 4.0**.

## Citation

If you use this dataset, please cite:

```bibtex
@article{Lvy2026,
  title = {Noninvasive decoding of typed sentences from human brain activity},
  ISSN = {1546-1726},
  url = {http://dx.doi.org/10.1038/s41593-026-02303-2},
  DOI = {10.1038/s41593-026-02303-2},
  journal = {Nature Neuroscience},
  publisher = {Springer Science and Business Media LLC},
  author = {Lévy,  Jarod and Zhang,  Mingfang and Pinet,  Svetlana and Rapin,  Jérémy and Banville,  Hubert and d’Ascoli,  Stéphane and King,  Jean-Rémi},
  year = {2026},
  month = June 
}
@article{zhang2025thoughtactionhierarchyneural,
      title={From Thought to Action: How a Hierarchy of Neural Dynamics Supports Language Production}, 
      author={Mingfang Zhang and Jarod Lévy and Stéphane d'Ascoli and Jérémy Rapin and F. -Xavier Alario and Pierre Bourdillon and Svetlana Pinet and Jean-Rémi King},
      year={2025},
      eprint={2502.07429},
      archivePrefix={arXiv},
      primaryClass={q-bio.NC},
      url={https://arxiv.org/abs/2502.07429}, 
}
```

## Acknowledgements

Supported by the Basque Government (BERC 2022–2025) and the Spanish State Research Agency
(BCBL Severo Ochoa accreditation). Parts of this work were carried out within the European
Union's Horizon 2020 programme under the Marie Skłodowska-Curie grant agreement No 945304
(Cofund AI4theSciences, PSL University).