ruvnet--RuView/docs/adr/ADR-250-adaptive-gamma-entrainment.md

ADR-250: Adaptive Gamma Entrainment Using RuVector and RuView

Field	Value
Status	Proposed
Date	2026-06-09
Owner	RuView, RuVector, RuFlo clinical systems
Decision type	Architecture, safety, research platform
Scope	Personalized noninvasive sensory stimulation and passive state sensing
Codebase target	v2/crates/ruview-gamma (this ADR's reference implementation)

Not medical advice: This ADR defines a research and engineering architecture. It does not define an approved Alzheimer's treatment. The immediate product claim is personalized entrainment optimization, never Alzheimer's treatment.

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1. Context

Recent research suggests that noninvasive 40 Hz multisensory stimulation using light and sound can influence gamma neural activity and may activate glymphatic clearance pathways associated with amyloid removal in Alzheimer's mouse models. The strongest mechanistic support comes from a 2024 Nature paper showing that multisensory gamma stimulation promoted cerebrospinal fluid influx, interstitial fluid efflux, aquaporin-4 polarization, meningeal lymphatic dilation, VIP interneuron signaling, and amyloid clearance in 5XFAD mice. Blocking glymphatic clearance abolished the amyloid-clearing effect.

Human evidence is promising but still early. A 2022 study in mild probable Alzheimer's disease reported that 40 Hz sensory stimulation was feasible and well tolerated, with exploratory signals around brain structure, connectivity, sleep, and memory. A small 2025 long-term pilot reported daily 40 Hz audiovisual stimulation over two years was safe and feasible and may slow cognitive and biomarker progression, but the sample size was very small and not definitive.

The field mostly treats 40 Hz as a fixed protocol. That is a useful population prior, but individual brains may differ by baseline gamma state, arousal, sleep quality, sensory acuity, medication, age, disease stage, and comfort tolerance. A 2025 PLOS One study re-evaluated gamma stimulation frequency across 36–44 Hz, supporting the idea that frequency choice should be empirically measured rather than assumed.

2. Problem

Fixed 40 Hz stimulation creates four engineering limits:

1. It assumes the same frequency works for everyone.
2. It does not continuously verify entrainment.
3. It ignores physiological state (sleep, motion, breathing, restlessness, comfort).
4. It cannot safely optimize stimulation parameters over time.

For clinical or wellness-grade deployment, the system must answer: which frequency, modality, intensity, timing, and session structure produces the strongest safe entrainment for this person in this state?

3. Decision

We build an Adaptive Gamma Entrainment Architecture where 40 Hz is the initial prior, not the hard-coded answer. The system uses:

1. RuView as the passive state-sensing layer.
2. RuVector as the personal response-modeling layer.
3. A constrained optimizer to select stimulation parameters.
4. RuFlo as the governed workflow, audit, safety, and protocol-execution layer.
5. Clinical-mode separation to prevent unsupported therapeutic claims.

The system optimizes stimulation only within a predefined safety envelope and separates entrainment optimization from disease-outcome claims.

4. Architecture Overview

Person baseline → RuView passive sensing → optional EEG → stimulus session
   → response extraction → RuVector personal response vector
   → constrained optimizer → next best protocol → RuFlo audit + governance

Component	Role	Output
RuView	Passive sensing of body and environment	breathing, motion, posture, stillness, sleep state, adherence
EEG (optional)	Direct entrainment measurement	gamma power, phase locking, artifact score
Stimulus controller	Light + sound actuator	frequency, intensity, phase, duty cycle, duration
RuVector	Learns personal response surface	individual entrainment vector
Optimizer	Selects next safe stimulation setting	recommended protocol
RuFlo	Governance and audit	protocol record, safety log, reproducibility trail

5. Stimulus Search Space

Parameter	Default range	Notes
Frequency	36–44 Hz	published exploratory range
Starting prior	40 Hz	strongest preclinical literature
Modality	audio, visual, combined	combined preferred (GENUS-style)
Brightness	bounded low–moderate	avoid unsafe flicker intensity
Volume	bounded low–moderate	comfort-constrained
Duty cycle	continuous, ramped, pulsed	start conservative
Phase	synchronized, offset	explore only after baseline
Duration	short calibration first	longer only after tolerance
Time of day	morning, evening, quiet wake	state-dependent

6. Personal Response Vector

RuVector represents each person with a compact 20-field adaptive vector (baseline_gamma, baseline_alpha, alpha_gamma_ratio, gamma_power_gain, phase_locking_value, breathing_rate, breathing_stability, motion_artifact, posture_state, sleep_state, restlessness_score, stimulus_frequency, brightness_level, sound_level, duty_cycle, phase_offset, session_duration, comfort_score, adherence_score, adverse_event_flag), updated after each session: R_{t+1} = update(R_t, stimulus_t, response_t, safety_t).

7. Optimization Objective

The optimizer maximizes safe, stable entrainment, not raw gamma power:

score =  w1·gamma_power_gain + w2·phase_locking_gain + w3·breathing_stability
       + w4·adherence + w5·comfort
       − w6·motion_artifact − w7·adverse_event_risk − w8·overstimulation_penalty

Default weights: gamma 0.30, phase-locking 0.25, comfort 0.15, breathing 0.10, adherence 0.10, motion penalty 0.05; safety penalty is a hard constraint, not negotiable.

8. Learning Method (staged loop)

• Phase 1 — Conservative calibration: short sessions at 36–44 Hz (1 Hz steps).
• Phase 2 — Bayesian optimization: GP surrogate + Expected Improvement, subject to safety==true ∧ comfort≥threshold ∧ adverse_event_risk≤threshold.
• Phase 3 — Contextual bandit: once enough sessions exist, LinUCB over state context → stimulus action → safe-entrainment reward.
• Phase 4 — Closed-loop control: mid-session, bounded frequency nudges when entrainment drops, intensity reduction on discomfort, scoring pause on motion spikes, and hard terminate-and-lock on adverse events.

9–12. RuView / RuVector / RuFlo roles & Safety

RuView supplies non-camera passive context (breathing, motion, posture, stillness, restlessness, sleep proxy, interference, adherence). RuVector supplies adaptive memory (personal vector, session-to-session learning, anonymized nearest-neighbor, drift detection, clustering, recommendation, edge inference) and predicts safe-entrainment / comfort / artifact / adherence likelihood — not Alzheimer's improvement. RuFlo governs (consent, inclusion/exclusion, scheduling, safety-stop rules, parameter audit trail, ADR linkage, model-version tracking, clinician export, trial-mode separation). Every session is reproducible via session_hash = hash(protocol_version, model_version, device_version, stimulus_parameters, sensor_summary, response_summary, safety_events).

Hard-stop conditions: headache, dizziness, nausea, agitation, visual discomfort, abnormal distress, seizure-like symptoms, user-stop request, sensor confidence below threshold, protocol outside approved envelope.

Exclusion / clinical supervision: epilepsy or seizure history, photosensitivity, severe migraine sensitivity, severe psychiatric instability, implanted neurological devices, significant sensory impairment affecting protocol validity, medication changes affecting neural response. The system must never autonomously expand beyond the allowed safety envelope.

18. Acceptance Criteria

Criterion	Target
Frequency control precision	±0.1 Hz
Session audit completeness	100%
Motion artifact detection	≥90% valid/invalid classification
Adaptive protocol improvement	≥20% entrainment gain vs fixed 40 Hz
Comfort	no worse than fixed 40 Hz
Safety stops	100% logged
Repeatability	same optimal band within ±1 Hz across 3 sessions
Claim discipline	no disease-treatment claim in product UI

19. Non-Goals

This ADR does not claim: RuView treats Alzheimer's; RuVector clears amyloid; RF sensing measures amyloid directly; personalized frequency improves clinical outcomes; consumer deployment is safe without screening; 40 Hz is always optimal.

21. Implementation Roadmap → reference crate ruview-gamma

Milestone	Module(s) in ruview-gamma	Status in this ADR's impl
M1 Simulator	simulator.rs (deterministic ChaCha20 response surface)	Implemented
M2 Device harness (contract)	stimulus.rs, safety.rs (envelope + emergency stop)	Interfaces + safety implemented
M3 RuView integration (contract)	response.rs (RuViewState)	State contract implemented
M4 EEG validation (contract)	response.rs (EegMeasurement), objective.rs	Optional input implemented
M5 Adaptive optimizer	optimizer.rs (Phase 1+2), bandit.rs (Phase 3), closed-loop	Implemented
M6 Trial mode	ruflo.rs (consent, inclusion/exclusion, sham, audit, session hash)	Implemented
§10 RuVector self-learning	ruvector.rs (anonymized ProfileStore, deterministic kNN, cohort warm-start priors via down-weighted GP pseudo-observations, physiological drift detection, deterministic clustering)	Implemented

The crate is a deterministic, dependency-light leaf (no internal RuView deps, ChaCha20 PRNG, SHA-256 witness — same discipline as nvsim), so the optimizer, safety envelope, and RuVector update logic can be tested and replayed bit-exactly before any hardware or human exposure. Hardware actuation, real RF sensing, and real EEG land behind feature flags / external adapters; this crate implements the governed software core and its proofs.

23. Platform Generalization — Adaptive Sensory Neuromodulation

The broader opportunity is adaptive sensory neuromodulation, not just Alzheimer's. 40 Hz is one prior in one program; the engine is a personal neural-rhythm optimization platform. RuView turns the body into the feedback signal, RuVector turns repeated sessions into a personal response map, the device is the actuator, and RuFlo makes the loop governed and auditable.

Each use case is a NeuroProgram (program.rs) bundling its own safety envelope, starting prior, objective weighting, physiological-state gating, evidence level, and the single non-disease claim it may surface:

Program	Evidence level	RuView / RuVector role	Released claim
alzheimers-research	Medium preclinical, early human	Adaptive entrainment + trial monitoring	personalized entrainment optimization
post-stroke-cognition	Early human	Recovery-state tracking (ramped, comfort-weighted)	…with recovery-state monitoring
sleep-optimization	Early but plausible	Time stimulation to sleep state (audio, near-dark cap)	sleep-state-timed entrainment optimization
attention-working-memory	Mixed / protocol-dependent	Personal frequency discovery (entrainment-weighted)	personalized frequency-response discovery
mood-arousal	Early human	Avoid overstimulation, tune calming response	personalized calming-response optimization
home-wellness	Speculative	Safe personalization without treatment claims	personal neural-rhythm wellness optimization
trial-infrastructure	Strong infrastructure	Governed protocol/safety/consent/sham log	governed, reproducible protocol measurement

Claim discipline is structural. A program's claim is always an optimization/monitoring statement, never a disease-treatment claim; the disease context lives only in EvidenceLevel. A claim is releasable only through the acceptance gate.

23.1 Generalized acceptance gate (acceptance.rs)

Every use case must show measurable entrainment, safety, adherence, and repeatability before making any disease claim.

AcceptanceHarness::evaluate(program, person, state) runs the program over ≥3 independent repeats and measures: adaptive-vs-fixed-prior entrainment gain, safety-stop rate, mean adherence, and the spread of the discovered optimal frequency. The resulting AcceptanceReport exposes a ClaimGate that returns the program's claim iff all four criteria pass, and the research-only NO_CLAIM string otherwise — the program's marketing claim cannot be read except through this gate. This makes the acceptance sentence executable, not aspirational, and applies uniformly to all seven programs.

22. Final Decision Statement

We build Adaptive Gamma Entrainment as a governed RuView + RuVector architecture. The system treats 40 Hz as the evidence-based starting prior, then learns each person's safe entrainment response curve using passive sensing, optional EEG, constrained optimization, and auditable RuFlo workflows. The immediate product claim is personalized entrainment optimization — not Alzheimer's treatment. That distinction keeps the system scientifically credible, clinically safer, and commercially defensible.