Bio-stimulation Algorithm for Targeted Neural Control
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Solution Overview
Problem
Current cranial nerve stimulation methods, both invasive and non-invasive, face limitations in accuracy and safety due to reliance on trial and error processes, with non-invasive methods being particularly inefficient in targeting specific brain areas and potentially causing prolonged stimulation times and safety issues.
Innovation Solution
A method and apparatus that derive a systematic algorithm from bio-related information using time-series data to determine the optimal bio-stimulation signal, applying it to the living body through a stimulation unit controlled by a control unit, which analyzes bio-responses to refine the stimulation protocol, minimizing trial and error by establishing a matrix-based relationship between bio-stimulation and bio-response information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If non-invasive stimulation methods are used to avoid surgical risks, then safety is improved, but stimulation accuracy deteriorates requiring trial and error processes
Solution Approach 1:
The system continuously measures bio-information (neural activity, muscle response, etc.) during stimulation and uses this feedback to automatically adjust stimulation parameters in real-time, creating a closed-loop control system that maintains accuracy without trial-and-error
Solution Approach 2:
The system dynamically changes multiple stimulation parameters (intensity, frequency, pulse width, electrode position) based on real-time bio-information analysis, allowing precise targeting of specific brain regions while maintaining safety through automated control
2Measurement precision
If trial and error stimulation is performed to achieve accurate targeting, then stimulation accuracy may be improved, but stimulation time increases causing safety problems
Solution Approach 1:
The system performs preliminary identification of target brain regions using bio-information measurement and pattern recognition algorithms before applying stimulation, pre-determining optimal electrode positions and parameters to eliminate iterative trial-and-error processes
Solution Approach 2:
The system replaces the mechanical trial-and-error adjustment process with an automated computational system that uses algorithms to calculate optimal stimulation parameters based on measured bio-information, significantly reducing adjustment time
3Ease of operation
If conventional command sets based on experience rules are used, then implementation is simplified, but precision and accuracy are limited due to incomplete knowledge
Solution Approach 1:
The system automatically determines optimal stimulation parameters by analyzing bio-information and applying computational algorithms, eliminating the need for operator expertise and experience-based decision-making while maintaining high precision
Solution Approach 2:
The system integrates multiple sources of information (neural activity patterns, anatomical data, physiological responses) and combines them through computational algorithms to create a comprehensive control strategy that exceeds the precision of any single experience-based rule
Data Source
AI summary
An apparatus for more accurately stimulating a living body comprises: a stimulation unit configured to apply a bio-stimulation signal in vicinity to a living body, the bio-stimulation signal being composed of pieces of time-series data having a specific frequency; and a control unit configured to derive bio-stimulation information required to achieve targeted bio-information using time space data indicative of bio-responses interacting at a plurality of different positions in response to the bio-stimulation signal, and derive a relation between the bio-stimulation signal and the bio-responses, and control the stimulation unit to apply the bio-stimulation signal in response to the derived bio-stimulation information. The relation is configured to set the bio-stimulation information as variables in an X matrix (m, t), set the bio-response information as variables in a Y matrix (n, t), and derive an A matrix (n, m) satisfying Y=AX.