Neural Interface Using Offline Separation Matrix for Real-Time EMG Decoding
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Solution Overview
Problem
Current neural interfaces and human-machine interfaces face challenges in providing reliable and efficient methods for decoding motor neuron activity, particularly in real-time applications such as controlling prosthetics or rehabilitation devices, due to limitations in signal processing and separation techniques.
Innovation Solution
The development of a neural interface apparatus that utilizes a training module to generate a separation matrix based on initial electromyography signals, allowing for the detection of motor neuron action potentials in real-time through a decomposition module, which processes surface electromyography signals from an electrode array, enabling accurate decoding of motor neuron activity for controlling external systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional neural interface signal processing methods are used, then the system structure is simple, but the measurement precision and reliability of motor neuron activity decoding are insufficient
Solution Approach 1:
The patent segments the signal processing into distinct functional modules: a training module for offline separation matrix generation, and a decomposition module for real-time motor neuron activity detection. This segmentation allows complex blind source separation to be performed offline, while real-time processing remains computationally manageable, thus improving measurement precision without excessively increasing real-time device complexity.
Solution Approach 2:
The patent applies preliminary action by performing the computationally intensive blind source separation and separation matrix generation during an offline training phase before actual use. This preliminary processing creates a ready-to-use separation matrix that can be applied in real-time without requiring complex real-time computation, thereby improving decoding accuracy while keeping real-time system complexity low.
2Productivity
If real-time decomposition is implemented, then the productivity and responsiveness are improved, but the measurement precision may be compromised compared to offline processing
Solution Approach 1:
The separation matrix is pre-computed during an offline training phase using recorded EMG signals and blind source separation algorithms. This preliminary action allows the computationally intensive separation process to be completed beforehand, enabling real-time decomposition to simply apply the pre-computed matrix through efficient matrix multiplication, thus maintaining both real-time performance and high measurement precision.
Solution Approach 2:
The patent creates a computational model (separation matrix) during offline training that replicates the complex signal separation process. This copied representation allows real-time processing to achieve accurate motor neuron decomposition through simple matrix operations, bridging the gap between offline accuracy and real-time speed.
3Measurement precision
If blind source separation algorithms are used, then the measurement precision of individual motor neuron detection is improved, but the loss of time for processing increases
Solution Approach 1:
The patent divides the blind source separation process into two temporal segments: an offline training phase where the separation matrix is generated using comprehensive EMG data, and a real-time phase where only efficient matrix multiplication is performed. This segmentation allows the computationally intensive accuracy-improving algorithms to run when time is not critical, while real-time operation uses optimized computations.
Solution Approach 2:
The separation matrix generation is performed as a preliminary offline step before actual motor neuron detection is needed. This preliminary computation of the separation matrix using blind source separation algorithms allows accurate motor neuron decomposition to be achieved in real-time through efficient matrix multiplication, eliminating the time penalty during critical real-time operation.
Data Source
AI summary
Surface electromyography signals of a nervous system are obtained; a separation matrix is generated based on electromyography signals obtained over a first time period using a training module; one or more motor neuron action potentials for single motor neurones are detected based on said electromyography signals and said separation matrix, wherein said electromyography signals are provided over a second time period shorter than said first time period; and an output is generated in the form of a time-series indicative of motor neuron activity.