Neural Stimulation Analyzer Using Behavioral Data Feedback
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Solution Overview
Problem
Current neural stimulation systems for treating neurological disorders, such as deep brain stimulation (DBS), face challenges in determining optimal stimulation parameters like frequency and pulse duration, which can be time-consuming and not suitable for real-time interventions, affecting the efficacy of treatments.
Innovation Solution
A computer-implemented neural stimulation system that analyzes muscle contractions and movements to derive neural data, assess the neural state, and determine appropriate stimulation parameters, using cloud computing and machine learning techniques to provide real-time adjustments in stimulation parameters based on cognitive and behavioral data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional neural stimulation parameter determination methods are used, then treatment coverage is comprehensive, but the process is time-consuming and not suitable for real-time interventions
Solution Approach 1:
The system continuously monitors behavioral data and neural signals, comparing actual outcomes against predicted outcomes to automatically adjust stimulation parameters in real-time. This closed-loop feedback mechanism enables dynamic optimization without manual intervention, resolving the contradiction between comprehensive treatment and time efficiency.
Solution Approach 2:
The neural stimulation system performs self-adjustment of parameters by autonomously processing behavioral data and neural signals to determine optimal stimulation settings. This self-service capability eliminates the need for continuous manual parameter determination, enabling real-time adaptation while maintaining comprehensive treatment coverage.
2Productivity
If manual determination of stimulation parameters is performed, then parameter accuracy can be ensured, but the process is not suitable for real-time interventions
Solution Approach 1:
The system introduces behavioral data and neural signals as intermediary measurements that objectively quantify neural state and treatment response. These intermediaries enable automated, real-time parameter determination with high precision, replacing manual judgment while maintaining or improving accuracy through continuous objective measurement.
Solution Approach 2:
The system dynamically changes stimulation parameters based on real-time analysis of behavioral data and neural signals. By continuously adjusting frequency, amplitude, and other parameters according to measured neural state, the system achieves both high productivity through automation and high precision through adaptive optimization.
3Adaptability or versatility
If standardized neural stimulation protocols are used, then treatment consistency is maintained, but personalization for individual patients is limited
Solution Approach 1:
The system transitions from static, standardized protocols to dynamic, adaptive treatment that continuously adjusts parameters based on real-time behavioral data and neural signals. This dynamic approach maintains consistency through systematic decision-making while achieving personalization through individual-specific response patterns and continuous optimization.
Data Source
AI summary
Embodiments are directed to a computer implemented neural stimulation system having a first module configured to derive neural data from muscle contractions or movements of a subject. The system further includes a second module configured to derive a neural state assessment of the subject based at least in part on the neural data. The system further includes a third module configured to derive at least one neural stimulation parameter based at least in part on the neural state assessment. The system further includes a fourth module configured to deliver neural stimulations to the subject based at least in part on the at least one neural stimulation parameter.