01 / SENSE
Sense neural dynamics
Fuse EEG, EMG, eye movement, and task telemetry to capture continuous intent under real-world noise.
Brain-computer AI / 01
Intent
before language.
A living interface between neural dynamics adaptive models and the machines around us.
NEURobinhood Chain
Contract address0xcB8F0103c7F7ca153baa10753cDd5F4835eEef82
Token live
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LIVE RESEARCH FEED NEURAL LATENT SPACE / ADAPTIVE DECODING / CLOSED-LOOP CONTROL / HUMAN-IN-THE-LOOP EVALUATION / SIGNAL QUALITY: 0.94
Featured case / Voice
Thought
to voice.
A brain-computer interface that turns intended speech into a personalized voice — restoring a channel between inner language and the world.
Explore the VOICE case ↗01 / CAPTURENeural signal
02 / DECODESpeech intent
03 / PERSONALIZENatural voice
04 / STREAMAudible output
System architecture / 03
A model stack
for unstable signals.
Neuronaq runtime
We treat neural data as a dynamic system: heterogeneous, personal, and only meaningful inside a task.
EEG0.88 SNR
EMG0.76 SNR
Gaze0.92 SNR
Context0.81 fit
01 ingest(signal, context)
02 normalize(subject, session)
03 infer(intent, uncertainty)
04 route(policy, actuator)
05 observe(feedback)
06 update(latent_state)
// every prediction carries
// a confidence envelope.
02 normalize(subject, session)
03 infer(intent, uncertainty)
04 route(policy, actuator)
05 observe(feedback)
06 update(latent_state)
// every prediction carries
// a confidence envelope.
Representation layerTemporal convolution + transformer memoryAdaptive
Inference layerIntent / arousal / motor-state decompositionProbabilistic
Safety layerConfidence gates, abstention, human overrideAlways on
Platform / 02
A neuroadaptive stack for turning noisy biological signals into machine-readable context.
02 / MODEL
Build a personal brain model
Adapt to each person’s signal grammar while preserving stable representations across sessions and states.
03 / RESPOND
Close the feedback loop
Translate model outputs into assistive, robotic, and generative actions that learn from every interaction.
The interface should not ask people to become more machine-like. It should make machine intelligence more human-aware.
20msTarget end-to-end feedback latency for responsive closed-loop control.
4×Signal families fused to reduce single-sensor noise and drift.
1:1One adaptive model per person, rather than one average answer.
Validation protocol / 04
Evidence
before autonomy.
01 / ACQUIRE
Collect
Capture multimodal neural data during real tasks, not isolated lab gestures.
02 / DECODE
Infer
Separate intent, uncertainty, workload, and motor state into interpretable outputs.
03 / VERIFY
Stress-test
Measure cross-session drift, false positives, and abstention under distribution shift.
04 / CLOSE LOOP
Adapt
Return feedback to the model only when confidence and human control remain intact.
Research programs / 05
Three starting points for a general-purpose neural interface—each with a different failure mode, each requiring its own evidence.
Silent control for complex tools
Decode low-bandwidth intent into cursor, language, and robotic actions without forcing users into a fixed command vocabulary.
Models that know when not to act
Estimate uncertainty, fatigue, and task context so the system can abstain, ask, or wait instead of confidently guessing.
Interfaces that learn both ways
Study how people and models co-adapt over time while preserving agency, reversibility, and an observable chain of decisions.
