Brain Stimulation System Using Concept Lattices
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Solution Overview
Problem
Current methods for determining optimal brain stimulation patterns for inducing desired behaviors are inefficient, relying on trial-and-error processes and lacking a systematic approach to analyze large-scale neural data effectively.
Innovation Solution
A system utilizing a brain monitoring and stimulation subsystem with processors and a computer-readable medium to analyze multi-scale distributed data, transform it into a graphical representation, and apply electrical current stimulation to achieve specific behavioral effects, employing self-organized criticality for electrode location and parameter optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If trial-and-error methods are used to determine optimal brain stimulation patterns, then the system can eventually find effective stimulation parameters, but the process is extremely time-consuming and inefficient
Solution Approach 1:
The system performs preliminary actions by pre-processing and analyzing large-scale neural data to identify patterns and relationships before actual stimulation occurs. The graphical representation and concept lattice are built in advance from experimental data, enabling rapid determination of optimal stimulation parameters without repeated trial-and-error processes.
Solution Approach 2:
The patent introduces intermediary structures including a graphical representation of neural data and a concept lattice that mediates between raw neural data and stimulation parameter determination. These intermediaries transform complex neural data into actionable insights, significantly reducing the time needed to identify effective stimulation patterns.
2Adaptability or versatility
If simulated annealing is used for optimization, then the system can handle complex optimization problems, but it requires tedious tuning of annealing parameters and is slow
Solution Approach 1:
The patent replaces the mechanical optimization process of simulated annealing with a data-driven approach using graphical representations and concept lattices. This substitution eliminates the need for tedious parameter tuning and reduces computational time by directly analyzing neural data patterns rather than relying on iterative optimization algorithms.
Solution Approach 2:
The system changes the approach from adjusting optimization parameters (as in simulated annealing) to analyzing and transforming data parameters. By converting neural data into graphical representations and concept lattices, the system identifies optimal stimulation parameters directly from data patterns rather than through iterative parameter adjustment.
3Ease of manufacture
If stimulation is applied to general brain regions like prefrontal cortex, then the approach is simple and based on coarse neural understanding, but the precision and effectiveness are limited
Solution Approach 1:
The patent applies local quality by identifying specific, localized brain regions and neural circuits based on detailed analysis of neural data patterns. Instead of applying stimulation to general regions like the prefrontal cortex, the system pinpoints precise locations and parameters derived from the graphical representation and concept lattice, significantly improving induction precision while maintaining systematic methodology.
Solution Approach 2:
The system creates a computational model (copy) of neural data relationships through graphical representations and concept lattices. This model allows precise prediction and determination of optimal stimulation parameters by analyzing the copied neural patterns, enabling high-precision behavioral induction without requiring complex real-time neural manipulation.
4Reliability
If brute-force trial methods are used with multiple electrodes and intensity levels, then all possible stimulation patterns can be tested, but the number of trials becomes prohibitively large
Solution Approach 1:
The patent extracts key information from the vast space of possible stimulation patterns by analyzing neural data patterns and identifying critical features through graphical representations and concept lattices. This extraction approach eliminates the need to test all possible patterns brute-force, while still achieving complete and reliable determination of optimal parameters by focusing only on the most relevant patterns identified through data analysis.
Data Source
AI summary
Described is a system for inducing a desired behavioral effect using an electrical current stimulation. A brain monitoring subsystem includes monitoring electrodes for sensing brain activity, and a brain stimulation subsystem includes stimulating electrodes for applying an electrical current stimulation. Multi-scale distributed data is registered into a graphical representation. The system identifies a sub-graph in the graphical representation and maps the sub-graph onto concept features, generating a concept lattice which relates the concept features to a behavioral effect. Finally, an electrical current stimulation to be applied to produce the behavioral effect is determined.