Neural Interface EMG Signal Segmentation for Prosthetic Control
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Solution Overview
Problem
Current neural interfaces face challenges in effectively decoding and independently controlling artificial devices without hindering natural motor functions, as they often rely on limited frequency bands and interfere with existing motor control mechanisms.
Innovation Solution
The apparatus and method utilize a filter module to separate neuron discharge patterns into frequency band signals based on bandpass filter configuration parameters, generating independent control signals that can be used to control external devices or software applications, including prosthetics, without interfering with natural muscle functions, by extracting information from higher frequency bands not directly related to motor control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If neural interfaces use limited frequency bands for control signals, then the control mechanism is simpler, but the ability to independently control artificial devices without interfering with natural motor functions is reduced
Solution Approach 1:
The patent segments the EMG signal spectrum into multiple frequency bands (e.g., 5-15 Hz, 15-30 Hz, 30-45 Hz) using bandpass filters. Each frequency band is processed independently to generate separate control signals, allowing simultaneous control of multiple degrees of freedom without interference between natural motor functions and artificial device control
Solution Approach 2:
The patent transitions from temporal signal processing to spectral domain processing by applying Fourier transforms and analyzing frequency components. This dimensional change from time-domain to frequency-domain enables extraction of independent control signals that are not apparent in the temporal waveform alone
2Ease of operation
If neural interfaces extract control signals from EMG frequencies directly related to motor control, then the control signal extraction is more straightforward, but natural motor functions are hindered
Solution Approach 1:
The patent extracts control information from frequency bands that are typically not associated with voluntary motor control (e.g., higher frequency bands above 30 Hz). By taking out control signals from these non-traditional frequency ranges, the system enables artificial device control without interfering with natural motor functions that operate in lower frequency bands
Solution Approach 2:
The patent applies different processing strategies to different frequency bands based on their characteristics. Low-frequency bands are processed to preserve natural motor control, while high-frequency bands are processed to extract additional control degrees of freedom for artificial devices
3Productivity
If neural interfaces decode multiple control signals from EMG data, then the number of controllable degrees of freedom increases, but the decoding complexity increases
Solution Approach 1:
The patent changes the parameter space by transforming EMG signals from time-domain waveforms to frequency-domain representations using spectral analysis. This parameter transformation reveals independent control dimensions in different frequency bands, enabling decoding of multiple simultaneous control signals with improved computational efficiency
Data Source
AI summary
An apparatus, computer program and method is described comprising: separating neuron discharge patterns obtained from electromyography signals into one or more frequency band signals based on bandpass filter configuration parameters and converting one or more of said one or more frequency band signals into a one or more control signals, wherein said one or more control signals include one or more first control signals, wherein the or each first control signal is an independent augmented control signal.