From supervised experiment to usable product
A laboratory can ask a participant to repeat known movements while a technician recalibrates a decoder. An everyday assistive device cannot depend on that ritual every morning. It has to interpret today's changed neural signals without today's labels already being available.
Few-Shot Unsupervised is the benchmark category that tests that missing capability. VERITY's result does not make a clinical claim; it demonstrates a practical path for removing one of the barriers between a controlled BCI experiment and a marketable neural prosthetic.
The product test is simple: can the interface adapt to the person today, without asking the person to help retrain it first?
What is different
The system does not hide everything inside one aggregate score. It measures whether the evidence for each prediction is strong enough to trust, and reports a confidence posture for every timestep.
The operator sees the predicted movement and how strongly the evidence supports it. Not just “the arm moved here”, but “the arm moved here, and this is how strongly the evidence supports that prediction.”
A system that always produces an answer is not a verification system.
Public leaderboard position
FALCON H1 · Human 7-DOF Reach and Grasp · public EvalAI leaderboard, observed September 2026. Sorted by held-out R² mean (↑).
| Rank | Team | Held out R² | Held in R² | Out σ | Category |
|---|---|---|---|---|---|
| 1–6 | FALCON baselines (Oracle / FSS, deep nets) | 0.44–0.63 | n/a | n/a | Not FSU |
| 7 | LM Meyer (RNN, zero-shot) | 0.40 | 0.62 | 0.14 | ZS |
| 8 | Credasis (VERITY FSU) | 0.30 | 0.30 | 0.07 | FSU · #1 |
| 9 | SPINT (Gradient-Free FSU) | 0.29 | 0.47 | 0.15 | FSU |
#1 in Few-Shot Unsupervised: VERITY leads SPINT on held-out R² (0.30 vs 0.29) and on consistency: held-out variance 0.07 vs 0.15, with held-out matching held-in at 0.30 where SPINT falls 0.47 to 0.29. Ranks 1–6 are organizer Oracle/FSS baselines (they see labels or supervised calibration); rank 7 is a zero-shot RNN, a different category.
What FALCON measures
FALCON (Few-shot Algorithms for Consistent Neural decoding) is a community benchmark for intracortical brain-computer interfaces. Decoders trained on early sessions often fail on new days when neural signals drift. FALCON evaluates how well adaptation methods restore performance with only a small amount of calibration data on held-out days.
The H1 task uses 176-channel Utah-array recordings with 7 degrees-of-freedom movement decoding. No trial structure is exploited. Calibration adapts from each session's own held-out data, under 1 ms per timestep after warm-up.
Documented limits
| Limitation | Detail |
|---|---|
| DOF 5 (hand open/close) | R² = 0.21, primary bottleneck on this task |
| Session drift | Day-to-day scale and baseline shift remain a challenge |
| Temporal state | Per-timestep predictions; no cross-bin memory claimed |
We report every session, including the ones where performance is weakest.
Specifications
| Model size | 0.2 MB |
| Hardware | CPU only |
| Per-timestep latency | ~7 ms |
| Normalized latency | ~0.40 |
| Neural bin size | 20 ms (50 Hz) |
| Air-gapped | Yes |
| Deep learning at inference | None |
| Calibration data used | 100% of held-out calib |
| Operator parameters | Sensitivity |
| Reproducibility | MD5-tracked |
| Channels / DOF | 176 / 7 |
| Posture | Per-timestep confidence |
Same philosophy, new surface
Quantum verification, network anomaly detection, and intracortical decoding: one commitment across domains. Reproducible evidence, transparent limits, and calibration without forcing new labels on the user.