Closed-loop neuromodulation system with reinforcement learning
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Solution Overview
Problem
Current neuromodulation devices operate in open-loop conditions, leading to side effects and the need for frequent adjustments, as they do not account for changing patient brain states, resulting in suboptimal treatment outcomes.
Innovation Solution
A closed-loop neuromodulation system that includes a sensor, recording amplifier, and processor to actively sense brain states and provide non-binary stimulation, using reinforcement learning to adjust stimulation parameters based on neural dynamics and patient-specific conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If open-loop constant high frequency stimulation is used, then the device structure is simple, but side effects increase and treatment efficacy decreases
Solution Approach 1:
The patent implements closed-loop control by continuously monitoring neural activity through recorded signals and adjusting stimulation parameters in real-time based on the detected brain state. The controller receives feedback from the neural tissue and modulates stimulation delivery accordingly, transitioning from open-loop constant stimulation to adaptive closed-loop stimulation that responds to actual physiological conditions.
Solution Approach 2:
The patent transforms the static, constant stimulation approach into a dynamic system where stimulation parameters (frequency, amplitude, pulse width) are continuously adjusted based on real-time neural activity detection. The system adapts its stimulation delivery according to the detected brain state, making the therapy dynamic rather than fixed.
2Ease of operation
If open-loop constant stimulation is applied, then the device is easy to operate, but frequent adjustments are required
Solution Approach 1:
The patent enables the device to self-adjust stimulation parameters automatically based on real-time neural activity monitoring. The controller autonomously modifies stimulation delivery according to detected brain states without requiring external intervention or manual reprogramming, allowing the device to serve itself in optimizing therapy parameters.
Solution Approach 2:
The closed-loop system continuously monitors neural responses and uses this feedback to automatically adjust stimulation parameters, eliminating the need for frequent manual adjustments by clinicians. The system self-regulates based on therapeutic efficacy indicators detected from the patient's neural activity.
3Device complexity
If simple threshold measurements with binary stimulation are used, then the device complexity is low, but treatment efficacy is limited
Solution Approach 1:
The patent moves beyond binary on/off stimulation by continuously varying multiple stimulation parameters (frequency, amplitude, pulse width, duty cycle) based on detected neural activity patterns. This multi-parameter modulation allows for nuanced control of neural responses, enabling more effective treatment while maintaining manageable device complexity through systematic parameter adjustment.
4Device complexity
If constant high frequency stimulation is used, then the device structure is simple, but neural adaptation occurs and off-target circuits are recruited
Solution Approach 1:
The patent implements dynamic stimulation where parameters are continuously adjusted based on real-time neural activity detection, preventing neural adaptation to constant stimulation patterns. The system modulates frequency, amplitude, and other parameters adaptively, ensuring that stimulation remains effective without recruiting off-target circuits through excessive or inappropriate activation.
Data Source
AI summary
The neuromodulation system includes a sensor, a recording amplifier, a processor, and a stimulator. The neuromodulation system is configured to provide stimulation and control of an intended target. The processor utilizes a closed-loop feedback system which is configured to actively sense target brain states and apply corrective stimulation or feedback as dictated by its effectors. The processor implements reinforcement learning which creates real-time statistical models of current and recent past neural states which actively and automatically learns stimulation paradigms which create paths from pathological to nominal brain states.