Neural Activation Estimation Using Decomposed Response Signals
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Solution Overview
Problem
Existing implantable electrical stimulation systems struggle to accurately determine which neural elements are activated by stimulation, as current methods do not reveal the type of neural fibers or populations activated, hindering precise stimulation targeting.
Innovation Solution
A method and system for estimating neural activation using signal decomposition, involving the identification of neural elements, obtaining neural response signals, and decomposing these signals using template signals to determine weights, adjusting weights based on differences, and filtering to improve stimulation targeting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signal decomposition methods are applied to neural response signals, then measurement precision of neural activation is improved, but device complexity increases
Solution Approach 1:
The neural response signal is segmented into distinct components by decomposing it into template signals, each representing different neural elements. This allows the complex signal to be broken down into manageable parts that can be individually analyzed and identified, improving measurement precision while maintaining manageable system complexity through structured signal separation.
Solution Approach 2:
Template signals serve as intermediaries between the raw neural response signal and the final neural activation estimation. These templates act as mediators that facilitate the decomposition process, enabling the system to translate complex neural responses into identifiable patterns without requiring direct complex analysis of the original signal.
2Measurement precision
If multiple template signals are used for decomposition, then neural element identification accuracy is improved, but computational requirements increase
Solution Approach 1:
The template signals are prepared and stored in advance before actual neural activation estimation is needed. This preliminary preparation allows the decomposition process during operation to simply involve matching and weighting existing templates rather than computing new patterns, reducing real-time computational requirements while maintaining high identification accuracy.
Solution Approach 2:
Instead of directly analyzing the complex neural response signal in its original form, the system creates simplified copies in the form of template signals that represent different neural elements. These templates are then weighted and combined to reconstruct the neural activation pattern, reducing computational complexity while preserving identification accuracy through the use of simplified signal representations.
Data Source
AI summary
A method for estimating neural activation arising from stimulation by a stimulation system includes identifying different neural elements stimulated by the stimulation; obtaining a neural response signal resulting from the stimulation by the stimulation system; and decomposing the neural response signal to estimate neural activation of each of the different neural elements.