Modélisation cyclostationnaire et séparation de sources des signaux électromyographiques

The aim of this thesis is to develop decomposition methods of electromyographic (EMG) signals into elementary signals, called motor unit action potential trains (MUAPT). We proposed two signal generation models and we have demonstrated the cyclostationary and fuzzy cyclostationary properties of these. We finally proposed a blind decomposition method from multi-sensor EMG signals using these properties. We present the theoretical limitations of the method, in particular the existence of a limiting threshold of the discharge frequency. We conducted a performance evaluation of the proposed method with a comparison with conventional 2nd order separation method. It has been shown that the contribution of cyclostationarity property brings better performance in noisy and noiseless cases and in the cyclostationary and fuzzy cyclostationary model cases. We highlighted a performance degradation when the discharge frequency was beyond the theoretical threshold. This evaluation was performed via Monte Carlo simulations based on real observations. Finally, we presented real EMG signals results. The method has shown good results on intramuscular EMG signals.L’objectif de cette thèse est de développer des méthodes de décomposition des signaux électromyographiques (EMG) en signaux élémentaires, les trains de potentiels d’action d’unité motrice (TPAUM). Nous avons proposé deux modèles de génération des signaux et nous avons mis en évidence la propriété de cyclostationnarité et de cyclostationnarité floue de ces deux modèles. Dans l’objectif de la décomposition, nous avons enfin proposé une méthode de décomposition aveugle à partir de signaux EMG multi-capteurs en utilisant cette propriété. Nous présentons les limitations théoriques de la méthode, notamment par un seuil limite de la fréquence de décharge. Nous avons effectué une évaluation des performances de la méthode proposée avec comparaison à une méthode classique de séparation à l’ordre 2.Il a été montré que l’exploitation de la propriété de cyclostationnarité apportait de meilleures performances de séparation dans le cas bruité et non bruité, sur le modèle cyclostationnaire et sur le modèle cyclostationnaire flou. Les performances se trouvent dégradées lorsque la fréquence de décharge dépasse le seuil théorique. Cette évaluation a été réalisée au moyen de simulations de Monte-Carlo construites sur des observations réelles. Enfin, la méthode appliquée sur des données réelles a montré de bons résultats sur des signaux EMG intramusculaires

On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden