| --- |
| 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). |
|
|
