Decoding motor neuron behavior for advanced control of upper limb prostheses

One of the main challenges in upper limb prosthesis control to date is to provide devices intuitive to use and capable to reproduce the natural movements of the arm and hand. One approach to solve this challenge is to use the same control signals for prosthesis control that our nervous system uses to control its muscles. This thesis aims to investigate the possibility of natural, intuitive prosthesis control using neural information obtained with available surface EMG decomposition methods. In order to explore all aspects of such a novel approach, a series of five studies were performed with the final goal of implementing a proof of concept and comparing its performance with state of the art myoelectric control. The performed investigations revealed important insights in motor unit physiology after targeted muscle reinnervation, EMG decomposition in dynamic voluntary contractions of the forearm, and the properties and challenges of neural information based prosthesis control. The main outcome of the thesis is that neural information based prosthesis control is capable to outperform myoelectric approaches in pattern recognition, linear regression and nonlinear regression, as determined by offline performance comparisons. The final proof of concept for this novel approach was a robust regression method based on neuromusculoskeletal modeling. The kinematics estimation of the proposed approach outperformed EMG-based nonlinear regression in both able-bodied subjects and patients with limb deficiency, indicating that using neural information is a promising avenue for advanced myoelectric control.2017-11-3

Predicting wrist kinematics from motor unit discharge timings for the control of active prostheses

BACKGROUND: Current myoelectric control algorithms for active prostheses map time- and frequency-domain features of the interference EMG signal into prosthesis commands. With this approach, only a fraction of the available information content of the EMG is used and the resulting control fails to satisfy the majority of users. In this study, we predict joint angles of the three degrees of freedom of the wrist from motor unit discharge timings identified by decomposition of high-density surface EMG. METHODS: We recorded wrist kinematics and high-density surface EMG signals from six able-bodied individuals and one patient with limb deficiency while they performed movements of three degrees of freedom of the wrist at three different speeds. We compared the performance of linear regression to predict the observed individual wrist joint angles from, either traditional time domain features of the interference EMG or from motor unit discharge timings (which we termed neural features) obtained by EMG decomposition. In addition, we propose and test a simple model-based dimensionality reduction, based on the physiological notion that the discharge timings of motor units are partly correlated. RESULTS: The regression approach using neural features outperformed regression on classic global EMG features (average R2 for neural features 0.77 and 0.64, for able-bodied subjects and patients, respectively; for time-domain features 0.70 and 0.52). CONCLUSIONS: These results indicate that the use of neural information extracted from EMG decomposition can advance man-machine interfacing for prosthesis control.peerReviewe

Representation of the decomposition of a single trial for subject T2.

<p>Above: one channel of the EMG signal. Below: bar plots of the decomposed spike trains and their MUAPs over the matrix. The colors of the spike trains and the MUAPs are matched. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0149772#pone.0149772.s001" target="_blank">S1 Fig</a> is a black and white version of this figure.</p

Mean and standard deviation of the investigated motor unit properties of the two groups.

<p>TMR patients are shown in black, able-bodied subjects in gray. The group means are depicted on the right side.</p

Number of decomposed spike trains per movement for each subject.

<p>Number of decomposed spike trains per movement for each subject.</p

Motor unit action potentials of a decomposed motor unit of subject T3 in all the channels (left) and the corresponding interpolated motor unit RMS map (right).

<p>One channel without a MUAP shape was contaminated by signal artefacts, and was excluded from the analysis (blank in the figure). The ellipse fitted on the RMS map of the motor unit is drawn in black on the right. Based on this fitting the motor unit in this example had a normalized MUAP surface area of 0.3.</p

Age, gender and amputation details of TMR patients.

<p>Age, gender and amputation details of TMR patients.</p

Spatial positions of the motor unit surface areas in each electrode grid (rows) for each subject (columns).

<p>Note that for this study, grid 1 for subject T2 and grid 2 for subject T1 were not used, since they were not covering the <i>m</i>. <i>pectoralis</i>. Surface areas with the same colour in a given grid correspond to motor units identified in the same movement. For able-bodied subjects, one colour represents the same contraction for each subject and for TMR patients the movement classes are different for each individual. The area on the grid occupied by motor units active during only one task is coloured in grey. The tick marks on the image borders denote 1 cm.</p

Spike triggered averaging based on the decomposed spike trains.

<p>The average waveform around the spiking instants are calculated and organized in an 8 by 8 structure for further processing.</p