System Overview
Mnemonic Guard synchronizes episodic memory recall, hand motor patterns, and EEG neural validation into a single ephemeral credential.
Three Authentication Layers
Episodic Memory
Deeply personal life-experience challenges that are nearly impossible to guess, phish, or infer from public data. AI-filtered "deep facts" ensure questions target memories unknown to outsiders.
Motor Fingerprinting
The device's built-in accelerometer and gyroscope capture the unique dynamics of your hand movement — not the shape, but your personal motor signature. No camera required.
Neural Validation (EEG)
Ear-EEG earbuds or lightweight headsets verify that the brain is genuinely recalling the enrolled memory and executing the movement, not fabricating or guessing.
Enrollment Process
The user enrolls by linking personal memories with unique motor patterns and neural signatures. The system creates interdependent templates that cannot be separated or replayed.
Memory Acquisition
A guided dialogue extracts deeply personal life events. AI filters out publicly discoverable facts (social media, records) and generates secure question-answer pairs. Answers are stored as salted cryptographic hashes.
Motor Fingerprint Capture
While recalling a specific memory, the user performs a hand movement. The device records 3-axis accelerometer and gyroscope data across 5–10 repetitions, building a motion-sensor template of the user's unique motor dynamics.
Neural Template Creation
Simultaneously, EEG sensors capture motor cortex activation (mu/beta rhythms) and episodic memory signatures (frontal-temporal theta/gamma). ML classifiers learn to distinguish authentic recall from fabrication.
Secure Template Storage
All templates are encrypted (AES-GCM), stored on-device or server, with deliberate obfuscation between question text and neural templates. Optional homomorphic encryption enables privacy-preserving server-side matching.
Authentication Process
During authentication, the system issues a challenge and simultaneously captures motion and neural data. The credential exists only temporarily — it cannot be stored, stolen, or replayed.
Challenge Issued
The system selects an enrolled memory and prompts: "Perform your movement while recalling the answer." Parameters like speed and repetitions are randomized to prevent replay attacks.
Concurrent Data Capture
Motion sensors and EEG record simultaneously, time-aligned. The user does not speak or type the answer — the system reads intent through movement and brain activity alone.
Triple Similarity Scoring
Three independent scores are computed: motion-sensor similarity (Smotion), motor EEG similarity (SmotorEEG), and episodic EEG authenticity (Sepisodic).
Combined Decision
A weighted composite score determines access: grant, require additional factor, or deny. The system can also detect stress, coercion, or duress markers and trigger security alerts.
Core Technical Features
No Camera Required
Authentication relies entirely on motion sensors (accelerometer, gyroscope) built into existing smartphones and wearables. No visual identification of hand shape or gesture path is needed.
Ear-EEG Integration
Designed for consumer-grade ear-EEG earbuds (2–6 channels) with dry electrodes. Neural validation works through everyday wearable hardware, not laboratory equipment.
Ephemeral Credentials
No static credential is ever stored in full form. The combined cognitive-neural-motor signature exists only during the live authentication moment and cannot be captured or replayed.
Anti-Coercion Detection
EEG and behavioral markers can detect physiological indicators of stress, coercion, or duress. The system can silently deny access or trigger a security alert when such conditions are detected.
Cryptographic Key Derivation
Optionally derives cryptographic keys from combined cognitive-neural-motor features for encrypting data, authorizing crypto transactions, or accessing secure enclaves.
Privacy-Preserving Matching
Homomorphic encryption allows similarity computations on encrypted templates without exposing raw EEG or motion data to any server. Data never leaves the encrypted domain.
Security Comparison
| Attack Vector | Traditional Auth | Mnemonic Guard |
|---|---|---|
| Password Guessing / Brute Force | Vulnerable | Resistant — No static password exists |
| Phishing | Vulnerable | Resistant — Answers are never typed or spoken |
| Biometric Spoofing | Vulnerable | Resistant — Requires live neural + motor signals |
| Credential Theft / Database Breach | Vulnerable | Resistant — No full credential stored |
| Replay Attack | Vulnerable | Resistant — Randomized parameters each attempt |
| Coercion / Forced Unlock | Vulnerable | Resistant — Detects stress via EEG markers |
| AI-Assisted Social Engineering | Vulnerable | Resistant — Deep facts filtered by AI |
Applications
Cryptocurrency Wallets
Derive cryptographic keys from live cognitive-biometric sessions. No seed phrase to lose, no private key to steal.
Financial Transactions
High-value transaction authorization with coercion detection. The system won't authorize transfers under duress.
Secure Computing Environments
Access control for classified systems, secure enclaves, and password managers with multi-layer cognitive verification.
Beyond Passwords, Beyond Biometrics
Mnemonic Guard creates a fundamentally new class of authentication — where the credential is an ephemeral fusion of memory, movement, and neural state that only exists in the moment of authentication and cannot be separated, stored, or reproduced by anyone other than the enrolled user.