Non-invasive brain-to-text · Open source
From EEG to open-vocabulary text
NEST is a hardware-agnostic decoding stack that turns non-invasive EEG recorded during reading into text, and reports its numbers the way the field should: free decoding, held-out subjects, noise controls.
Research prototype. No validated benchmark yet. The first word error rate ships together with its evaluation protocol.
press start to decode the illustrative sample
Status · September 2026
Where the project stands
Every number on this site is verified against the repository. When something is not measured yet, it says pending.
We withdrew a number. In February 2026 we published a word error rate that had been estimated from training loss, not measured by decoding. It was removed. Results appear here only when they are decoded on held-out subjects with a noise-input control. Read the evaluation protocol.
Why now
Non-invasive brain-to-text left the lab in 2025
Three things changed at once: the research results, the capital, and the shape of the gap.
The results moved
Meta's Brain2Qwerty decoded typed sentences from MEG at a 32% character error rate in February 2025. The June 2026 v2 release reached 61% word accuracy. Those are MEG numbers on a closed, typing-style task; the same study's EEG condition scored 67% character error rate. Non-invasive decoding is improving quickly, and open-vocabulary EEG reading, the task NEST works on, is still unsolved.
The capital arrived
Venture funding for brain-computer interfaces went from roughly $0.4B in 2024 to about $1B in 2025. In the first seven months of 2026 the non-invasive share rose to 29%.
The gap is a software layer
Implant companies operate on patients. Headset companies sell hardware. The one vertically integrated text decoder, Sabi, only runs on its own cap. Nobody ships an open decoding layer that works on any EEG system.
How it works
Word-aligned EEG in, sentence out
NEST follows the ZuCo feature convention: for every word a reader fixates, 105 channels times 8 frequency bands become one 840-dimensional vector. A sequence of those vectors is one sentence. ZuCo is by Hollenstein et al. (2018), released under CC BY 4.0.
Hardware-agnostic
NEST is software. It takes preprocessed features, so any research-grade EEG system that can produce them is a valid input. Transfer to low-channel consumer headsets is an open question we have not tested.
Subject-independent
Train on 8 subjects, validate on 1, test on 2 the model never saw. Personal calibration is a later feature, not a hidden assumption.
Open
MIT-licensed code, data loaders and evaluation scripts. The benchmark protocol is part of the product.
Evaluation
The field's numbers were inflated. Ours are built to survive that critique.
EEG-to-text papers on ZuCo reported BLEU-1 around 40. In 2024 the community found those scores came from teacher forcing, and that random noise scored about the same as real EEG.
Published
Teacher-forced scores
Wang and Ji (AAAI 2022) BLEU-1 40.1. DeWave (NeurIPS 2023) BLEU-1 41.4. BELT 42.3. All measured with the decoder given the correct previous word at every step.
Corrected
Free-decoding scores
Jo et al. 2024 showed noise input matches real EEG under that protocol. A July 2026 replication reports free-decoding WER above 99% for the same model family. Open-vocabulary EEG-to-text is unsolved.
What NEST commits to.
- Free autoregressive decoding at evaluation time, never teacher forcing. The code path is public; today it is greedy, beam search is planned.
- Held-out subjects that never appear in training or validation.
- A noise-input control reported next to every real-EEG number.
- The protocol published together with the first result, as a technical report.
What exists
Built in seven months, verified in the repository
| Component | Today | Status |
|---|---|---|
| Codebase | 24,137 lines of Python: band-pass and ICA preprocessing, six model architectures, training and evaluation scripts | Implemented |
| Data | ZuCo pipeline written and run on the real corpus: 66 GB raw (53 .mat files), 12 subjects, 105 channels, 12,071 sentence-subject pairs, NaN handling for unfixated words. The raw data is re-downloaded before the GPU run | Implemented |
| Models | NEST v2 CTC (45.3M parameters) and NEST v2 BART (184M total, 64.7M trainable) instantiate and run | Verified |
| Evaluation | Free autoregressive decoding (greedy today), greedy CTC decoding, word- and character-level edit distance | Implemented |
| Training | v1 model trained 100 epochs on 12,071 real samples (5.4 h on an M2 laptop); loss decreased, no WER computed. v2 needs a GPU | Pending GPU |
| Deployment | Pruning, quantization, ONNX and TorchScript export, streaming inference modules written | Unbenchmarked |
| Tests | 80 test functions; suite currently broken by code drift and is being repaired | In repair |
Who it is for
Research tool first, clinical product later
Sequenced the way non-invasive neurotech companies that reached revenue actually did it.
EEG research labs
A decoding SDK and an open benchmark for language-EEG research. Outside medical-device scope by design: no diagnostic or therapeutic claim.
Clinics and corpora
An ethics-approved Turkish reading corpus and research-only pilots with rehabilitation and neurology departments. The first Turkish EEG-to-text dataset.
Assistive communication
A CE-marked communication aid for people with ALS or locked-in syndrome who keep reading comprehension, delivered with EEG hardware partners.
Roadmap
The next twelve months
Each quarter ends with a measurable artifact: a benchmark report, a corpus release, a license count, a pilot protocol.
FAQ