AI-Driven Closed-Loop Brain Stimulation System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional brain monitoring and stimulation systems lack the capability to automatically refine parameter sets during operation, leading to inadequate detection and tracking of neurological diseases and treatment efficacy, as they fail to consider the initial state of neuronal regions and interplay.
Innovation Solution
A system comprising a processor, memory, and interconnected stimulation and sensing devices that use artificial intelligence to perform dynamic closed-loop feedback, adjusting stimulation parameters based on real-time sensed data from EEG, tDCS, tACS, tPBM, and other modalities for self-guided diagnostics and treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional brain monitoring and stimulation systems are used, then device complexity is reduced, but measurement precision and reliability of neurological disease detection and treatment tracking are insufficient
Solution Approach 1:
The system implements dynamic closed-loop feedback by continuously receiving sensed signals from multiple sensing devices, processing them through AI algorithms, and adjusting stimulation signals in real-time based on the feedback. This enables automatic refinement of parameter sets during operation, significantly improving measurement precision and treatment tracking without requiring complex manual intervention
Solution Approach 2:
The system performs self-guided, self-directed diagnostics and treatment by automatically refining parameter sets during operation using AI processing of sensed signals. The closed-loop system self-adjusts stimulation parameters based on real-time feedback, eliminating the need for continuous manual calibration and reducing operational complexity while maintaining high precision
2Adaptability or versatility
If conventional static parameter sets are used, then ease of operation is improved, but adaptability to individual patient conditions and treatment efficacy are insufficient
Solution Approach 1:
The system transitions from static parameter sets to dynamic parameter refinement by automatically adjusting stimulation parameters during operation based on real-time sensed signals and AI processing. This enables the system to adapt to individual patient conditions and treatment responses without requiring complex manual reconfiguration, maintaining ease of operation while significantly improving adaptability
Solution Approach 2:
The system automatically refines parameter sets during operation by changing stimulation parameters based on AI processing of sensed signals. This dynamic parameter adjustment enables the system to adapt to individual patient conditions and treatment efficacy variations while the automated process maintains operational simplicity
3Productivity
If manual parameter adjustment is used, then device complexity is reduced, but productivity and treatment efficiency are insufficient
Solution Approach 1:
The closed-loop feedback system continuously monitors treatment responses through sensed signals and automatically adjusts stimulation parameters in real-time, significantly improving treatment efficiency and productivity. The automated feedback mechanism eliminates time-consuming manual parameter adjustments while the integrated AI processing manages system complexity
4Reliability
If comprehensive sensing and stimulation devices are integrated, then measurement precision and treatment accuracy are improved, but device complexity increases
Solution Approach 1:
The system integrates multiple sensing devices (EEG, ECG, EMG, EOG, fNIRS, fMRI) and stimulation devices (tDCS, tACS, TMS, TENS, DBS) into a unified multi-functional platform that performs both comprehensive monitoring and treatment. The shared AI processing and closed-loop control architecture manage the complexity while enabling reliable diagnostics and treatment across multiple modalities
Data Source
AI summary
Embodiments may provide the capability to provide brain monitoring and stimulation devices using parameter sets that are automatically refined during operation. For example, in an embodiment, a system may comprise a plurality of stimulation devices connected to signal output circuitry interfacing the processor with the stimulation devices, so that the processor generates and transmits stimulation signals to the stimulation devices, and a plurality of sensing devices connected to signal input circuitry interfacing the processor with the sensing devices, so that the processor receives sensed signals from the sensing devices, wherein the program instructions and data stored in the memory are further configured so that the processor performs dynamic closed loop feedback of the stimulation signals based on the received sensed signals to provide self-guided, self-directed diagnostics and treatment of neural conditions using at least one recipe for a treatment strategy guided by artificial intelligence.