Closed-Loop Brain Stimulation for Adaptive TBI Mediation
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Solution Overview
Problem
Traditional neuronal signal modeling mechanisms are limited in accurately measuring and modeling complex neural systems, leading to inefficient and potentially erroneous brain stimulation due to over-stimulation and under-stimulation, and fail to account for cross-impacts of sub-systems or multiple modalities, resulting in inadequate treatment of conditions like traumatic brain injury.
Innovation Solution
A closed-loop adaptive therapeutic system utilizing machine learning and artificial intelligence to generate brain state parameters and models, enabling efficient and dynamic brain stimulation by continuously adjusting signals and models based on measured neural activity, incorporating hybrid therapies and pharmacological agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional neuronal signal modeling mechanisms are used, then the system is simple to implement, but the measurement precision and reliability of brain state characterization deteriorate
Solution Approach 1:
The system segments the complex brain modeling task into multiple components: neural activity measurement, brain state parameter generation, model generation, and procedure determination. Each component is handled by specialized processing devices, improving measurement precision while managing system complexity through functional decomposition
Solution Approach 2:
The system introduces intermediary processing devices that act as mediators between raw neural activity measurements and final treatment procedures. These intermediaries generate brain state parameters and models, transforming complex neural data into actionable insights without requiring the entire system to handle full complexity at once
2Reliability
If traditional brain stimulation methods are used, then the device complexity is low, but the reliability and effectiveness of treatment deteriorate due to over-stimulation and under-stimulation
Solution Approach 1:
The system implements continuous feedback loops where neural activity measurements inform brain state parameter updates, which in turn adjust model generation and procedure determination. This closed-loop feedback ensures treatment reliability by dynamically adapting to actual brain states, preventing both over-stimulation and under-stimulation
Solution Approach 2:
The system transitions from static brain stimulation protocols to dynamic, adaptive procedures. Processing devices continuously update brain state parameters and models based on real-time neural activity, allowing treatment parameters to evolve dynamically with the patient's changing brain state, thereby improving treatment reliability
3Adaptability or versatility
If traditional neuronal modeling is used, then the system is easy to operate, but the adaptability to individual patient conditions and cross-impacts of sub-systems deteriorates
Solution Approach 1:
The system applies local quality by generating patient-specific brain state parameters and individualized treatment procedures. Each patient's unique neural activity patterns are processed to create customized models and procedures, ensuring adaptability to individual conditions while the automated processing maintains ease of operation
Solution Approach 2:
The system achieves universality through multi-functional processing devices that can handle various types of neural activity measurements, generate multiple types of brain state parameters, create different models, and determine diverse treatment procedures. This multi-functionality provides patient-specific adaptability across different conditions while maintaining a unified operational interface
Data Source
AI summary
Provided are systems, methods, and devices for providing mediation of a traumatic brain injury. Systems may include an interface, processing devices, and a controller. The interface is configured to obtain measurements from a brain of a user with a traumatic brain injury. A first processing device is configured to generate multiple brain state parameters characterizing one or more features of a brain state of the user. A second processing device is configured to generate models of the brain of the user based on the plurality of brain state parameters and the plurality of measurements, and determine, using the models and training data comprising one or more mediation data points, a mediation procedure for reducing one or more symptoms of the traumatic brain injury. The mediation procedure is provided to one or more entities, and one or more control signals are generated by the controller based on the mediation procedure.