bcbl190626/SpanishBCBL

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:

  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:

  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.

Citation

If you use this dataset, please cite:

# 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).