Cochlear Implant ML Model Training via Brain Processing Feedback
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Solution Overview
Problem
Cochlear implant system performance is difficult to optimize due to the complexity of sound processing strategies, which depend on numerous variables and are challenging to objectively assess, often resulting in non-significant performance gains or decreased performance for certain users.
Innovation Solution
A machine learning model management system is implemented to train and maintain models for cochlear implant systems, using audio content as input to generate electrical signals, applying these signals to a brain processing model, calculating error metrics, and adjusting heuristics to improve sound processing strategies, thereby optimizing performance globally.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sound processing strategies are used, then the cochlear implant system can process audio content, but the performance optimization is difficult due to complexity of variables and challenging objective assessment
Solution Approach 1:
The patent introduces an intermediary objective assessment metric that mediates between the complex sound processing variables and the performance evaluation. This metric serves as a bridge that translates complex processing outcomes into measurable performance indicators, enabling objective assessment without directly managing the complexity of all underlying variables.
Solution Approach 2:
The patent transforms the complex multidimensional sound processing problem into a optimized set of parameters that can be objectively measured and adjusted. By changing the representation of processing strategy variables into optimized parameters, the system achieves performance optimization while reducing the apparent complexity of assessment.
2Reliability
If conventional approaches to improving sound processing strategies are used, then some performance gains may be achieved, but performance may decrease for certain users or use cases
Solution Approach 1:
The patent applies local quality optimization by tailoring sound processing strategies to individual user characteristics and specific use cases. Instead of a uniform approach, the system adjusts processing parameters locally for each user's auditory profile and environmental context, ensuring reliable performance across diverse users while maintaining high productivity in each specific case.
Solution Approach 2:
The patent implements dynamic adaptation of sound processing strategies that can adjust in real-time based on user feedback and environmental conditions. This dynamic approach allows the system to optimize performance for each user's specific needs while maintaining consistency across different users and situations, resolving the contradiction between reliability and productivity.
3Measurement precision
If complex sound processing strategies are implemented, then audio perception quality may improve, but power consumption increases
Solution Approach 1:
The patent extracts and separates the most critical processing functions from less important ones, applying complex processing only where necessary for audio perception quality. By taking out and selectively applying complex processing strategies only to essential audio components, the system maintains high audio quality while reducing overall power consumption of the processing unit.
Data Source
AI summary
An exemplary system is configured to maintain data representative a machine learning model for use in a cochlear implant system and train the machine learning model. The training may include applying audio content as a training input to the machine learning model, the machine learning model configured to apply a machine learning heuristic to the audio content to output an electrical signal representative of the audio content; applying the electrical signal to a brain processing model, the brain processing model configured to output synthesized audio content representative of the electrical signal; generating an error metric representative of a difference between the audio content and the synthesized audio content; and feeding back the error metric into the machine learning model, the machine learning model configured to use the error metric to adjust the machine learning heuristic applied to the audio content.