Brain Stimulation System with Real-Time EEG Feedback Control

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Solution Overview

Problem

Current brain stimulation therapies, such as deep brain stimulation (DBS), lack a systematic approach to optimize stimulation parameters in real-time based on individual brain activity, relying on manual adjustments and subjective feedback, which can lead to suboptimal efficacy and require trial-and-error methods.

Innovation Solution

A method and system that combines real-time monitoring of brain electrical activity with machine learning algorithms to adjust stimulation patterns dynamically, using denoised wavelet packet atoms and personalized mental state assessments to optimize brain stimulation for improved task performance and symptom reduction.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual adjustments and subjective feedback are used to optimize stimulation parameters, then the system is simple to operate, but the efficacy is suboptimal and requires trial-and-error methods

Engineering Contradiction:

Improveefficacy of brain stimulation therapyVSAvoidcomplexity of real-time monitoring and adjustment system

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system continuously monitors brain electrical activity and uses this feedback to dynamically adjust stimulation parameters in real-time, creating a closed-loop control system that optimizes therapy efficacy based on actual brain state rather than relying on manual trial-and-error adjustments

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system automatically adjusts stimulation parameters based on real-time brain activity monitoring and machine learning algorithms, enabling self-optimization without requiring continuous manual intervention or subjective patient feedback

Inventive Principle:

Principle #25Self-service

2Reliability

If real-time monitoring and dynamic adjustment are implemented, then the efficacy is improved, but the device complexity increases

Engineering Contradiction:

Improveefficacy of brain stimulation therapyVSAvoidease of manual parameter adjustment

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system performs automatic real-time optimization of stimulation parameters using machine learning algorithms and brain activity monitoring, eliminating the need for manual adjustments by clinicians and enabling the device to self-optimize therapy parameters dynamically

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system uses accelerated machine learning processing and real-time data analysis to rapidly optimize stimulation parameters, compensating for the increased computational complexity by enabling faster, more accurate parameter adjustment than manual methods

Inventive Principle:

Principle #38Strong oxidants (Accelerated oxidation)

3Productivity

If trial-and-error methods are used to optimize stimulation parameters, then the device complexity is low, but the time required for optimization is excessive

Engineering Contradiction:

Improvespeed of therapy optimizationVSAvoidtime for parameter adjustment

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system uses real-time feedback from brain electrical activity monitoring to immediately guide parameter adjustments, eliminating the time-consuming trial-and-error process by directly linking observed brain state to optimal stimulation settings through continuous monitoring and adaptive control

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system pre-processes brain activity data in real-time and uses machine learning models to predict optimal stimulation parameters before they are applied, enabling proactive optimization rather than reactive trial-and-error adjustment

Inventive Principle:

Principle #10Preliminary action

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This approach enables precise, real-time adjustment of stimulation parameters to enhance task performance and reduce symptoms in conditions like Parkinson's disease, dyskinesia, and anxiety disorders, improving the overall efficacy of brain stimulation therapies.

Implementation Method 1

EEG measures voltage fluctuations resulting from ionic current within the neurons of the brain

Methodology Applied

Scientific EffectIonic current: Conduction (electrical)

Data Source

PatentUS20250018201A1Systems and methods for cooperative invasive and noninvasive brain stimulation

Publication Date: 2025.01.16 NEUROSTEER INC

AI summary

Methods and systems for optimizing invasive and noninvasive brain stimulation are described herein. In a particular embodiment, methods and systems for a combinatorial, iterative approach to modify behavior are presented wherein deep brain stimulation (DBS) and other brain stimulation therapies are implemented in combination with monitoring the brain activity of an individual to optimize the effectiveness of the combinatorial approach to modify behavior. Methods described herein are iterative and systems described herein are utilized in iterative fashion. In a particular embodiment, modifying behavior provides a therapy for an individual in need thereof.