Surface EMG Motor Unit Decoding for FES Control
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Solution Overview
Problem
Current FES technologies face challenges in accurately measuring and decoding volitional intent for paralyzed patients due to the invasive nature of intracortical electrodes and the complexity of brain electrical activity, particularly in spinal cord injury and stroke rehabilitation, where surface EMG signals are attenuated and difficult to decode.
Innovation Solution
A wearable electrodes garment with surface electrodes and an electronic controller that extracts motor unit action potentials from surface EMG signals to identify intended movements and deliver functional electrical stimulation, using techniques like Convolutional Kernel Compensation decomposition and principal component analysis to decode volitional intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If intracortical electrodes are used to measure brain electrical activity for FES control, then volitional intent can be decoded, but the approach becomes invasive and complex
Solution Approach 1:
The patent uses surface EMG signals as an intermediary to indirectly measure volitional intent without requiring direct intracortical electrode insertion. The EMG signals serve as a mediator that captures motor unit activity resulting from cortical intent, providing a non-invasive pathway to decode movement intention while avoiding the complexities of direct brain electrode measurement
Solution Approach 2:
The patent replaces the mechanical/invasive intracortical electrode system with a non-invasive surface electromyography system. By substituting the measurement location from the brain cortex to the muscle surface, the system eliminates surgical implantation requirements while maintaining the ability to decode volitional intent through signal processing of EMG data
2Ease of operation
If surface EMG signals are used for FES control in paralyzed patients, then the approach is non-invasive, but the signals are attenuated and difficult to decode
Solution Approach 1:
The patent applies preliminary signal processing operations including filtering, rectification, and integration to the raw surface EMG signals before decoding. These preliminary actions enhance the signal quality and extract relevant features (such as RMS amplitude and activation timing) that improve decoding accuracy despite the attenuated nature of surface signals in paralyzed patients
Solution Approach 2:
The patent creates a simplified representation or copy of the original EMG signal by extracting key features such as root mean square amplitude, activation onset/offset timing, and motor unit action potential characteristics. This copied feature set retains the essential volitional intent information while being more robust to noise and attenuation, enabling accurate decoding without requiring high-fidelity raw signals
3Loss of information
If brain electrical activity is decoded for specific body part control, then volitional intent can be identified, but the complexity of entire body motor cortex activity makes this challenging
Solution Approach 1:
The patent segments the overall EMG signal into distinct motor unit action potentials and associates each with specific muscle groups or body parts. By decomposing the composite EMG signal into individual motor unit contributions, the system can identify which specific body part the patient intends to move without needing to decode the entire brain motor cortex activity pattern
Solution Approach 2:
The patent applies local analysis to specific regions of interest in the EMG signal corresponding to particular muscles or body parts. By focusing the decoding algorithm on localized EMG activity from specific electrode channels that correspond to target muscles, the system identifies specific body part intent without being overwhelmed by global brain activity complexity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables effective FES control and generates patient performance reports by accurately capturing volitional intent through surface EMG signals, even in paralyzed body parts, without the need for invasive electrodes, improving rehabilitation outcomes for SCI and stroke patients.
Implementation Method 1
receiving surface electromyography (EMG) signals via the electrodes
Implementation Method 2
extracting one or more motor unit (MU) action potentials from the surface EMG signals
Implementation Method 3
delivering functional electrical stimulation (FES) effective to implement the intended movement via the electrodes
Data Source
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AI summary
A therapeutic or diagnostic device comprises a wearable electrodes garment including electrodes disposed to contact skin when the wearable electrodes garment is worn, and an electronic controller operatively connected with the electrodes. The electronic controller is programmed to perform a method including: receiving surface electromyography (EMG) signals via the electrodes and extracting one or more motor unit (MU) action potentials from the surface EMG signals. The method may further include identifying an intended movement based at least on features representing the one or more extracted MU action potentials and delivering functional electrical stimulation (FES) effective to implement the intended movement via the electrodes of the wearable electrodes garment. The method may further include generating a patient performance report based at least on a comparison of features representing the one or more extracted MU action potentials and features representing expected and/or baseline MU action potentials for a known intended movement.