Electric Markov Blanket Control for Brain Criticality Synchronization
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Solution Overview
Problem
Existing technologies fail to quantify, reconstruct, or emulate the electrophysiological dynamics of the Markov blanket (Mb) for real-time neuromodulation, lack a neurocomputation model that synchronizes with human brain criticality, and do not respect user autonomy and privacy.
Innovation Solution
The Electric Markov Blanket (eMb) for the Brain Computer Criticality Bridge (BCCB) system acquires high-density electrophysiology data, computes a Criticality Vector (CV) using Bayesian active-inference models, and applies neuromodulatory outputs like transcranial electrical stimulation to maintain optimal excitatory-inhibitory balance, ensuring ethical control and data sovereignty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-density electrophysiology data is acquired and processed in real-time to quantify E-I criticality, then measurement precision and reliability improve, but device complexity and computational requirements increase
Solution Approach 1:
The patent introduces an intermediary computational layer that processes raw high-density electrophysiology data through Bayesian active-inference models to extract the Criticality Vector. This intermediary processing stage transforms complex multi-channel neural signals into a simplified representation (theta-gamma coupling for excitatory drive, alpha-beta coupling for inhibitory control) that maintains measurement precision while reducing the complexity of real-time analysis and control decisions.
2Adaptability or versatility
If a neurocomputation model synchronizes with human brain criticality for Brain Computer Criticality Bridge, then adaptability and information exchange improve, but loss of information regarding user autonomy and privacy increases
Solution Approach 1:
The patent applies local quality by processing and analyzing electrophysiology data locally at the user's device to compute the Criticality Vector and generate neuromodulatory outputs. Only aggregated, anonymized statistics are transmitted to remote servers, ensuring that sensitive neural information remains localized and user autonomy/privacy is preserved while still enabling adaptive brain-AI synchronization through the PNE model.
Solution Approach 2:
The system implements a feedback loop where the PNE model receives the Criticality Vector, generates appropriate neuromodulatory outputs (such as transcranial electrical stimulation parameters), and applies them to maintain optimal E-I balance. This closed-loop feedback enables continuous adaptation to the user's brain state while keeping the computation local, thereby preserving user autonomy and privacy.
3Productivity
If existing technologies attempt to quantify and reconstruct Markov blanket dynamics, then productivity and therapeutic capability improve, but reliability and ethical control decrease due to lack of user autonomy protection
Solution Approach 1:
The patent incorporates preliminary ethical control measures by designating an 'Ethical Control Layer' that operates alongside the therapeutic functions. This layer pre-establishes boundaries and constraints on data processing and neuromodulation, ensuring that user autonomy and privacy are protected from the outset rather than as an afterthought. The system is designed to respect user-defined boundaries before therapeutic interventions are applied.
Data Source
AI summary
An electric Markov blanket (eMb) system and method for establishing a Brain-Computer Criticality Bridge (BCCB) are disclosed. Electrophysiological signals are acquired from an organism, decomposed to obtain cross-frequency coupling metrics indexing excitatory-inhibitory criticality, and assembled into a Criticality Vector that fully characterizes the organism's electric Markov blanket. The Criticality Vector may be stored, analyzed, reproduced in vivo via patterned neuromodulation, or instantiated in silico or other informatic medium to create a personal neuromorphic emulation. Embodiments include ethical-control mechanisms ensuring user sovereignty and safety. The invention enables clinical interventions, cognitive enhancement, and personal-identity preservation.