Brain State Prediction for Personalized Non-Invasive Stimulation
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Solution Overview
Problem
Current technologies lack comprehensive understanding and predictive models of brain function, particularly in response to external and internal perturbations, and there is limited capability to simulate and optimize brain states for cognitive enhancement or disease management.
Innovation Solution
A system and method utilizing data analysis, neuromodulation, and AI algorithms to map, characterize, and predict brain function, enabling non-invasive brain stimulation to change brain states, and create personalized digital content and interventions based on individual brain metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive brain mapping and predictive modeling are implemented, then understanding of brain function improves, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the complex brain mapping system into multiple specialized modules: data acquisition module for collecting neural signals, data processing module for cleaning and organizing data, analysis module for extracting brain metrics, and predictive modeling module for simulating brain states. This segmentation allows each module to handle specific tasks efficiently, reducing overall system complexity while maintaining high measurement precision.
Solution Approach 2:
The patent introduces digital twins as intermediary computational models that simulate individual brain states and responses to interventions. These digital twins act as mediators between complex raw brain data and actionable insights, allowing researchers to predict brain responses without directly manipulating actual brains, thereby simplifying the experimental process while improving measurement accuracy.
2Reliability
If personalized brain stimulation protocols are developed, then cognitive enhancement effectiveness improves, but individual assessment and protocol customization time increase
Solution Approach 1:
The patent implements preliminary brain mapping and digital twin creation for each individual before cognitive enhancement interventions begin. This preliminary assessment establishes baseline brain states and predictive models that automatically guide subsequent stimulation protocols, eliminating the need for time-consuming trial-and-error customization during actual treatment and ensuring high effectiveness from the first intervention.
Solution Approach 2:
The patent incorporates continuous feedback loops where brain activity during stimulation is monitored in real-time, and the digital twin model is updated accordingly. This feedback mechanism allows the system to automatically adjust stimulation parameters based on individual responses, maintaining high effectiveness while minimizing manual intervention time and enabling rapid protocol optimization.
3Productivity
If real-time brain state monitoring and prediction is implemented, then intervention optimization improves, but computational resource requirements increase
Solution Approach 1:
The patent implements partial real-time monitoring by continuously tracking only the most relevant brain metrics that have the greatest impact on intervention effectiveness, while periodically updating less critical parameters. This selective monitoring approach maintains high intervention optimization capability by focusing computational resources on critical metrics, thereby reducing overall energy consumption while preserving productivity.
Data Source
AI summary
Methods and apparatus for changing a brain state of a person from an initial brain state to a target brain state are described. The method includes receiving information characterizing the initial brain state, the information including a structural composition and a functional architecture of the brain, estimating based, at least in part, on the received information, a potential for the brain to change from the initial brain state to the target brain state, determining based, at least in part, on the received information and the estimated potential for the brain to change from the initial brain state to the target brain state, a non-invasive brain stimulation protocol, and controlling at least one non-invasive brain stimulation device to stimulate the brain according to the non-invasive brain stimulation protocol. The method also includes using brain information to inform the design of computational general artificial intelligence agents.