Multi-Modal Neural Interface for Prosthetic Control
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Solution Overview
Problem
Current systems for controlling prosthetic devices are limited in accurately determining user movement intent from various physiological signals, as they often rely on single-mode sensors and struggle with combining information from multiple sensor types to provide a reliable estimate of intended movement.
Innovation Solution
A multi-modal neural interface system that receives and decodes multiple types of physiological activity signals, including LFP, spike, ECoG, EMG, and EEG signals, and fuses the movement intents into a joint decision to control prosthetic devices, using classifier and decode modules to process signals from various sensors and generate accurate motor control commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple types of physiological sensors are used to detect user movement intent, then the accuracy of movement intent estimation is improved, but the device complexity increases
Solution Approach 1:
The patent combines multiple physiological sensors (EMG, ECoG, LFP, spike sensors) into a unified neural interface system that processes signals from all sensor types simultaneously. The system merges data from these diverse sources through a common decoding framework to estimate movement intent, thereby improving measurement precision while managing system complexity through integrated architecture.
Solution Approach 2:
The neural interface system is designed with multi-functional capability to handle multiple signal types from different physiological sources. The decoding system can process and interpret signals from various sensor types (surface EMG, intramuscular EMG, ECoG, LFP, spike sensors) using a universal decoding framework, allowing the system to adapt to different sensor configurations while maintaining accurate movement intent estimation.
2Reliability
If information from multiple sensor types is combined, then the reliability of movement intent determination is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent introduces an intermediary decoding system that acts as a mediator between multiple physiological sensors and the prosthetic control system. This decoding framework processes and integrates signals from different sensor types (EMG, ECoG, LFP, spike), transforming complex multi-source data into reliable movement intent estimates. The intermediary decoder handles the complexity of signal integration, making the system more reliable without requiring direct complex processing at each sensor level.
Solution Approach 2:
The system employs parameter changes in the decoding process to optimize the integration of multiple signal types. By adjusting decoding parameters and weights for different sensor inputs based on their reliability and characteristics, the system can dynamically adapt to varying signal qualities and conditions, thereby improving movement intent determination reliability while managing the difficulty of processing diverse physiological signals.
Data Source
AI summary
Methods and systems to interface between physiological devices and a prosthetic device, including to receive a plurality of types of physiological activity signals from a user, decode a user movement intent from each of the plurality of signals types, and fuse the movement intents into a joint decision to control moveable elements of the prosthetic device.