Crosstalk Reduction in Epimysial EMG Recordings from Transhumeral Amputees with Principal Component Analysis

Electromyographic (EMG) recordings of muscle activity using monopolar electrodes suffer from poor spatial resolution due to the crosstalk from neighbouring muscles. This effect has mainly been studied on surface EMG recordings. Here, we use Principal Component Analysis (PCA) to reduce the crosstalk in recordings from unipolar epimysial electrodes implanted in three transhumeral amputees. We show that the PCA-transformed signals have, on average, a better signal-to-noise ratio than the original unipolar recordings. Preliminary investigations show that this transformation is stable over long periods of time. If the latter is confirmed, our results show that the combination of PCA with unipolar electrodes allows for a higher number of muscles to be targeted in an implant (compared with bipolar electrodes), thus facilitating 1-to-1 proportional control of prosthetic hands

Improving Suturing Skills for Surgical Residents and Advancing Prosthesis Control for Amputees.

Proper suturing technique is one of the most important skills a surgical resident should acquire. However, current methods for teaching it rely on subjective performance evaluations. An instrumented training apparatus for abdominal closure could be used to define objective assessments that directly relate to closure quality. I identify a synthetic material that models abdominal fascia using porcine and cadaveric data and design a means to mount the material so that it mimics abdominal closure. Digital images are used to quantify material deformations and provide real-time objective measures regarding the effect of suture placement and tension in the abdominal tissue. In parallel, I develop a finite element model of abdominal fascia and its closure with suture to deduce stresses in the material and forces in the sutures. I find that despite uniform suture spacing, the forces in suture are unevenly distributed along the closure. These findings motivate the development of a surgical learning tool that objectively relays information about suture placement and tension. In a second body of work, I address the development of a novel interface between an amputee’s peripheral nervous system and a motorized prosthetic device. Conventional myoelectric control cannot produce a sufficient number of independent signals for actuation of modern computerized upper limb prostheses. A compact construct involving grafted muscle surgically prepared at the end of a transected peripheral nerve is envisioned for transducing a nervous signal with fine specificity and sensitivity. Up to 20 such constructs can be prepared in a human arm, and epimysial electrodes on each construct can be used to relay signals encoding 20 independent channels of motor intent. I develop a means of evaluating this construct in awake rats, and demonstrate that the transduced signals suffer minimal crosstalk and are correlated with gait. A decoder is able to reconstruct data produced by motion tracking, and I show that adjacent constructs placed proximal to one another provide the same signals as anatomically intact muscle-nerve antagonist-pair analogs. The correlation between the signals transduced, the walking kinematics, and analogous out of phase activation obtained from adjacent constructs indicates that this technology holds promise for human translation.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147635/1/danursu_1.pd

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