Development and Validation of Experimental Protocol and Guidelines for Non-Invasive Superficial and Deep Muscle Electromyography in the Forearm - Brijlal, Yasheen (Division of Biomedical Engineering, 2019)
The present study investigated a novel non-invasive superficial and deep surface electromyography (sdEMG) technique to detect and isolate extrinsic muscles of the hand with the aim of developing experimental guidelines to aid future studies. The sdEMG technique comprises of two or more surface electrode arrays encircling the limb under investigation set up in a monopolar EMG recording modality and a blind source separation (BSS) algorithm to decompose the recorded mixed monopolar EMG signals into their constituent components, which is proposed to reflect the underlying EMG activity of each muscle. Three experimental parameters linked to the finger movement protocol (MP) were investigated that varied the effects of timing, randomisation and movement anticipation on the ability of the sdEMG technique to detect and isolate the superficial muscles flexor digitorum superficialis (FDS) and extensor digitorum (ED), and the deep muscles extensor indicis (EI), extensor pollicis longus (EPL), flexor digitorum profundus (FDP) and flexor pollicis longus (FPL). FDS and FDP were split into FDS-Index, FDS-Ring, FDP-Index and FDP-Ring bands resulting in a total of eight muscles investigated. A standard movement protocol consisting of 12 dynamic movements was designed to target the activation of the investigated muscles during each experimental run. The Timing experiments varied the movement window duration to 3, 5 and 7 seconds using the standard MP sequence. The Randomisation experiment consisted of a randomised MP sequence. The Anticipation experiment presented participants with the current, and next movement instruction in the standard MP sequence. The developed sdEMG system implemented 64 custom-made surface electrodes arranged in three bands positioned around the distal third of the forearm. An OT Bioelettronica® EMG-USB2 256-channel biopotential amplifier was used, set up in a referenced monopolar EMG configuration. Contraction detection apparatus was built consisting of finger exoskeletons and flex sensors to record when finger movements occurred. A forearm testing platform was built to secure the participant’s forearm during experimental testing and a visual participant instruction system was developed to convey the timed movement instructions. Five healthy, right-hand dominant male participants (mean ± SD; age: 24 ± 3 years) without any history of neuromuscular diseases or disabilities were recruited for the study. Each participant completed five experimental runs of the five MP variations while the EMG and flex sensor data was recorded. Independent Component Analysis (ICA) was used as the BSS algorithm and the EMG recordings were decomposed into Independent Components (ICs) which were further processed with a windowed 250ms root mean square (RMS) smoothing filter as well as signal normalisation. The flex sensor data was used to generate synchronised literature-informed predicted EMG (pEMG) waveforms, representing the ideal EMG activation signals for each muscle. The muscle-specific pEMG waveforms were also processed with a 250ms RMS filter and signal normalisation before signal comparisons were made using Pearson’s correlation against all pICs. In each experimental run, the pIC with the highest calculated Pearson’s correlation coefficient (r) value for each pEMG waveform was initially selected as the representative IC (rIC) for that muscle. A rIC selection algorithm was also developed which reassigned pICs that were selected to represent multiple muscles to ensure each muscle was assigned a unique rIC. A case study was conducted to evaluate the effects of the investigated movement protocol parameters upon which experimental guidelines were formed. Fisher-corrected mean population correlation coefficients (ρ) and 95% confidence intervals were calculated to evaluate the effects of timing, randomisation and anticipation of movements. Using an amalgamated population of all the experiments and experimental runs combined, the eight muscles investigated were isolated with ρ values greater than 0.65 indicating moderate isolation (defined as 0.60 ≤ ρ < 0.80), with the exception FDS-Index Band which was poorly isolated (ρ < 0.60) with a ρ value of 0.59. The data did, however, show high variability in all experiments indicating that the sample population was too small and was possibly influenced by poor performing participants. The Timing, Randomisation and Anticipation experiments showed no discernible effects across all participants on the ability of the sdEMG technique to detect and isolate the deep and superficial forearm muscles investigated. The Anticipation experiment also showed that participant reaction delays on average increased steadily during each experimental run suggesting the anticipated visual cues were too complex and potentially confused participants. Concise experimental sdEMG guidelines were developed in which the sdEMG technique was found to be robust to variations of the three movement protocol parameters investigated.