Decoding the Silent Mind.
Recent work has shown AI systems that interpret the vocalisations of animals. The far more consequential frontier is the inverse-facing one: people who have language but have lost the channel to express it.
The problem
Millions live with intact cognition but a broken output channel: locked-in syndrome, late-stage ALS, post-stroke aphasia, severe cerebral palsy, minimally-verbal autism. The bottleneck is not thought — it is the motor pathway between thought and speech. Eye-gaze keyboards and switch scanning are slow, fatiguing, and degrade as the condition progresses.
State of the art — and where it stops
Invasive BCIs (Neuralink; Stanford's Willett lab) reach the highest accuracy. Stanford has decoded inner speech — silent, unspoken language — from implanted motor-cortex arrays, hitting ~74% accuracy against a ~125,000-word vocabulary. Powerful, but it requires neurosurgery.
Non-invasive EEG decoding (DeWave; Meta's EEG-to-text work) skips surgery entirely: a snug sensor cap reads scalp signals, and a model paired with an LLM translates patterns into words.
Invasive
Non-invasive · our focus
The gap is sharp: invasive wins on accuracy but cannot scale; non-invasive is accessible but remains lab-bound — fragile across people, sessions, and electrical environments. Almost nothing addresses messy real-world deployment.
Our distinct angle — robustness, not raw accuracy
- Robustness across people and conditions. Domain adaptation so one model generalises across patient populations, cognitive states, electrical interference, and varying signal quality.
- Multimodal fusion. EEG carries the what of intended language. A wrist-worn device adds a parallel context channel — heart-rate variability, skin conductance — that cannot read thought but can disambiguate intent and flag urgency or distress.
- Real-time, low-latency decoding. Suitable for a bedside or care-home conversation, not a thirty-second-per-word crawl.
Why now
- Demand. Ageing populations, rising stroke and ALS prevalence, and a global shortage of speech-language assistive infrastructure.
- Enabler. Foundation models and cheap, portable EEG hardware have crossed a practicality threshold in the last eighteen months.
- White space. The field is crowded with proof-of-concept papers and near-empty on clinically deployable, person-agnostic systems. That is the research contribution.
Next · 03 — Beyond the Interface