---
pretty_name: "DECOMEG: Brain Activity During Typing (MEG & EEG)"
license: cc-by-nc-4.0   # TODO: confirm the final license for the public release
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)
│   │   └── <idx>_<code>/         # one directory per recording (idx 01–25; a few
│   │       └── <YYMMDD>/         #   participants have a second re-recording dir)
│   │           ├── block1.fif    # task blocks
│   │           ├── block2.fif
│   │           ├── tapping.fif   # finger-tapping localizer
│   │           └── ...           # (naming varies slightly across sessions)
│   └── logs/                     # behavioral logs (MATLAB .mat), participants S1–S25
│       ├── S<n>-session<k>-<b>.mat
│       ├── S<n>-session<k>_tapping.mat
│       ├── trials_S<n>-...mat
│       └── intertrials/          # per-trial inter-key-interval logs
└── EEG/
    ├── EEG/                      # raw EEG in BrainVision format (.eeg/.vhdr/.vmrk)
    │   └── <id>_DECOMEG_S<sess>_<code>_<task>.{eeg,vhdr,vmrk}
    └── logs/                     # behavioral logs (.mat)
        └── intertrials/
```

### File counts (this release)

| | Files |
|---|---|
| MEG raw `.fif` | 231 (across 29 recording directories) |
| MEG behavioral `.mat` (top-level) | 263 (incl. 123 `trials_*.mat`) |
| MEG `intertrials/*.mat` | 132 |
| EEG recordings (`.eeg`/`.vhdr`/`.vmrk` triplets) | 117 each |
| EEG behavioral `.mat` (top-level) | 220 |
| EEG `intertrials/*.mat` | 88 |
| Total size | ≈ 262 GB |

**Notes on naming**
- MEG `FIF/<idx>_<code>` directories are indexed `01–25`; the trailing `<code>` is an internal
  recording identifier, **not** a participant ID. A few participants have a second directory
  (e.g. a re-recording), so there are 29 directories for 25 participant indices.
- The date-coded sub-directory (`YYMMDD`) gives the acquisition date; raw `.fif` files within are
  split per block, plus `tapping`/`typing` runs. Some blocks are split into continuation files
  (`...-1.fif`).
- Behavioral logs use a separate participant numbering (`S1`–`S25`). The `block<n>_list<m>`
  fields denote the sentence block and stimulus list; `tapping` files are the motor localizer.

## File formats

- **`.fif`** — Elekta/Megin/MNE raw MEG. Load with [MNE-Python](https://mne.tools):
  ```python
  import mne
  raw = mne.io.read_raw_fif("MEG/FIF/01_9228/220404/Block1.fif", preload=True)
  ```
- **`.vhdr` / `.eeg` / `.vmrk`** — BrainVision EEG (header / data / markers). Load with:
  ```python
  raw = mne.io.read_raw_brainvision("EEG/EEG/001_DECOMEG_S1_10754_task1.vhdr", preload=True)
  ```
- **`.mat`** — MATLAB behavioral logs (stimuli, keystrokes, timing). Load with
  `scipy.io.loadmat` or MATLAB.

## 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** (to be confirmed).

## Citation

If you use this dataset, please cite:

```bibtex
# TODO put actual nature neuro citation
@article{levy2025brain2qwerty,
  title   = {Brain-to-Text Decoding: A Non-invasive Approach via Typing},
  author  = {L{\'e}vy, Jarod and Zhang, Mingfang and Pinet, Svetlana and Rapin, J{\'e}r{\'e}my
             and Banville, Hubert and d'Ascoli, St{\'e}phane and King, Jean-R{\'e}mi},
  journal = {arXiv preprint arXiv:2502.17480},
  year    = {2025}
}
@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).