ML Neural Interface for Closed-Loop Bodily Function Control
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Solution Overview
Problem
Current neural interface systems face challenges in capturing and interpreting bodily variables from neural activity, particularly for advanced applications requiring fine control of bodily functions, as existing methods are inefficient and lose information during signal processing.
Innovation Solution
A neural interface system utilizing machine learning techniques to process neurological signals, enabling accurate estimation and representation of bodily variables, and generating neural stimulus signals for closed-loop control and operation of devices like prosthetics or organ functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning techniques are used to process neurological signals, then measurement precision of bodily variables is improved, but device complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components between neural signal acquisition and bodily variable estimation. These ML models act as mediators that transform raw neurological signals into meaningful bodily variable estimates, enabling high measurement precision while keeping the overall system architecture manageable through modular deployment.
Solution Approach 2:
The patent replaces traditional signal processing mechanisms with machine learning-based processing. Instead of using conventional filtering and feature extraction methods, the system employs trained neural networks and ML algorithms to directly estimate bodily variables from neurological signals, achieving superior measurement precision.
2Productivity
If machine learning techniques are used to process neurological signals, then productivity of neural interface system is improved, but loss of information increases
Solution Approach 1:
The patent implements feedback mechanisms where the neural interface system continuously monitors neurological signals, processes them through machine learning models to estimate bodily variables, and uses this information to adjust stimulation parameters in real-time. This closed-loop feedback ensures that information is preserved and utilized effectively to improve system productivity.
Solution Approach 2:
The patent employs preliminary training of machine learning models using extensive datasets of neurological signals and corresponding bodily variable measurements. This preliminary action of training the ML models beforehand ensures that when the system operates, it can efficiently process signals with minimal information loss, thereby improving productivity.
3Adaptability or versatility
If machine learning techniques are used to process neurological signals, then adaptability of neural interface system is improved, but device complexity increases
Solution Approach 1:
The patent implements dynamic machine learning models that can adapt to individual subjects' neural characteristics. The system dynamically adjusts processing parameters, model configurations, and stimulation strategies based on real-time neural signal analysis, enabling high adaptability to different users and applications while managing complexity through adaptive algorithms.
Solution Approach 2:
The patent utilizes parameter changes in machine learning models to achieve adaptability. By adjusting model parameters, training data selections, and processing configurations based on individual subject characteristics and application requirements, the system becomes versatile across different neural interface applications without requiring fundamentally different hardware architectures.
Data Source
AI summary
Method(s) and apparatus are provided for interfacing with a nervous system of a subject. In response to receiving a plurality of neurological signals associated with the neural activity of the first portion of nervous system: processing neural sample data representative of the received plurality of neurological signals using a first one or more machine learning (ML) technique(s) trained for generating estimates of neural data representative of the neural activity of the first portion of nervous system; and transmitting data representative of the neural data estimates to a first device associated with the first portion of nervous system; and in response to receiving device data from a second device associated with a second portion of the nervous system: generating one or more neurological stimulus signal(s) by inputting the received device data to a second one or more ML technique(s) trained for estimating one or more neurological stimulus signal(s) associated with the device data for input to the second portion of nervous system; and transmitting the one or more estimated neurological stimulus signal(s) towards the second portion of nervous system of the subject.