Novel Methods for Recording and Analysing High-Density EMG Signals of the Hand & Forearm

Abstract

Electrical potentials are responsible for activating and coordinating skeletal muscle movement in the human body. These potentials, called myoelectric activity, can be registered and analysed using non-invasive recording electrodes in a technique known as surface electromyography (sEMG). Key information about the muscles can then be extracted from EMG signals using signal processing techniques and used in various applications ranging from electrophysiology studies to robotics. More recently, high-resolution multi-electrode arrays have expanded to the field of EMG, a technique named high-density electromyography (HD-EMG). This technique retains the beneficial aspects of sEMG while increasing the resolution of the collected data, which can provide an improved understanding of the spatial distribution and electrophysiological principles of myoelectric activity. These new insights into the mechanisms of voluntary muscle contraction can also be used to improve myoelectric human-machine interfaces, which allow users to have an embodied interaction with robotic devices using intuitive controls. This thesis focuses on the development of improved methods for recording and analysing high-density EMG signals from the hand and forearm to apply them to design novel machine-human interfaces for robotic control. A variety of flexible high-density electrodes were designed and fabricated to efficiently and accurately record myoelectric activity from the hand and forearm. Among these, a set of novel electrode arrays was developed to specifically target the intrinsic hand muscles. These consisted of 60 individual electrodes with a 4 mm inter-electrode distance (IED) divided in two arrays. The individual electrodes were arranged in groups targeting ten intrinsic muscles on the anterior and posterior sides of the hand. The hand arrays were paired with a set of tessellating rectangular arrays of 32 electrodes with a 7 mm IED, which can cover an area of up to 50 × 170 mm around the arm or forearm to record 256 EMG signals simultaneously. All arrays were constructed using a flexible substrate with a thickness less than 0.11 mm, which allowed users to retain full mobility during experimental tasks. Signal processing methods for HD-EMG were developed, including noise reduction with finite impulse response (FIR) and infinite impulse response (IIR) filters; data analysis with signal segmentation, feature extraction, data reduction and interpolation techniques. For visualisation, the distribution of myoelectric activity was imaged by transforming HD-EMG data into 2D spatiotemporal maps. These maps allowed for a graphical representation of muscle activity during voluntary movement. The applied signal processing methods were then paired with machine-learning (ML) algorithms to develop a framework for classifying HD-EMG signals using feature extraction and pattern recognition. This framework relied on the extraction of features from EMG signals which could then be used to train models capable of predicting the intended user motion based on myoelectric information. Five time-domain (TD) features, zero crossings (ZC), slope sign changes (SSC), waveform length (WL), and Willinson amplitude (WAMP), frequency-domain (FD) features such as median frequency (MDF), and temporal-spatial domain (TSD) features were calculated and used in four types of ML classifiers, support-vector machines (SVM), linear discriminant analysis (LDA), neural networks (NN) and k-nearest neighbour (KNN) algorithms. HD-EMG data was collected from 26 subjects while performing several different motions. Studies were performed to determine the optimal configuration of the classification framework according to different tasks. The influence of segmentation parameters, channel count and feature selection was evaluated to determine their weight over the accuracy of pattern-based classifiers. Among the different parameters, channel count was found to have a significant impact. Classification accuracy ranged from 74±4% when using only 5 bipolar channels, to 92±2% with 41 channels recording EMG signals from hand muscles to classify grasps and finger motions. In general, combinations of hand and wrist motions were decoded with an accuracy ranging from 75 − 98% using HD-EMG data collected from intrinsic and extrinsic muscles across the different experiments using the developed classification framework. The improved spatiotemporal resolution of the arrays was also used to visually track the propagation of motor-unit action potentials (MUAPs) to estimate the conduction of muscle fibres, which yielded 4.7±0.3 m/s and were in range with previously reported literature values using invasive EMG techniques. Based on the analysis of the different factors that influenced the accuracy of HD-EMG classification, the electrode arrays and classification framework were applied to two applications for robotic control. The first was a virtual environment constructed using the Webots robotic simulation platform, where a digital twin of an anthropomorphic robot was controlled using HD-EMG signals to interact with objects within a fully rendered 3-D environment by executing different hand grasps and motions. The second was the application of HD-EMG electrode arrays towards the development of a control scheme for dexterous object manipulation using robotic hands based on regression-based motion decoding. Regression models were trained using HD-EMG signals and motion-tracking as inputs to decode the position and trajectory of an object. The models achieved an accuracy of 63±7% and a correlation of 81±5% between the real and predicted objects. These models were then applied to control an under actuated anthropomorphic robot hand using HD-EMG inputs. The work in this thesis presents an overview of the design, optimization and incorporation of HD-EMG techniques into myoelectric control tasks. The proposed new technical approaches can also contribute to electrophysiological research and improve the quantity and quality of decoded motions in classification-based EMG interfaces.