Advances in surface EMG signal simulation with analytical and numerical descriptions of the volume conductor

Surface electromyographic (EMG) signal modeling is important for signal interpretation, testing of processing algorithms, detection system design, and didactic purposes. Various surface EMG signal models have been proposed in the literature. In this study we focus on 1) the proposal of a method for modeling surface EMG signals by either analytical or numerical descriptions of the volume conductor for space-invariant systems, and 2) the development of advanced models of the volume conductor by numerical approaches, accurately describing not only the volume conductor geometry, as mainly done in the past, but also the conductivity tensor of the muscle tissue. For volume conductors that are space-invariant in the direction of source propagation, the surface potentials generated by any source can be computed by one-dimensional convolutions, once the volume conductor transfer function is derived (analytically or numerically). Conversely, more complex volume conductors require a complete numerical approach. In a numerical approach, the conductivity tensor of the muscle tissue should be matched with the fiber orientation. In some cases (e.g., multi-pinnate muscles) accurate description of the conductivity tensor may be very complex. A method for relating the conductivity tensor of the muscle tissue, to be used in a numerical approach, to the curve describing the muscle fibers is presented and applied to representatively investigate a bi-pinnate muscle with rectilinear and curvilinear fibers. The study thus propose an approach for surface EMG signal simulation in space invariant systems as well as new models of the volume conductor using numerical methods

Mimicking human neuronal pathways in silico: an emergent model on the effective connectivity

International audienceWe present a novel computational model that detects temporal configurations of a given human neuronal pathway and constructs its artificial replication. This poses a great challenge since direct recordings from individual neurons are impossible in the human central nervous system and therefore the underlying neuronal pathway has to be considered as a black box. For tackling this challenge, we used a branch of complex systems modeling called artificial self-organization in which large sets of software entities interacting locally give rise to bottom-up collective behaviors. The result is an emergent model where each software entity represents an integrate-and-fire neuron. We then applied the model to the reflex responses of single motor units obtained from conscious human subjects. Experimental results show that the model recovers functionality of real human neuronal pathways by comparing it to appropriate surrogate data. What makes the model promising is the fact that, to the best of our knowledge, it is the first realistic model to self-wire an artificial neuronal network by efficiently combining neuroscience with artificial self-organization. Although there is no evidence yet of the model's connectivity mapping onto the human connectivity, we anticipate this model will help neuroscientists to learn much more about human neuronal networks, and could also be used for predicting hypotheses to lead future experiments

Distribution of motor unit potential velocities in short static and prolonged dynamic contractions at low forces: use of the within-subject’s skewness and standard deviation variables

Behaviour of motor unit potential (MUP) velocities in relation to (low) force and duration was investigated in biceps brachii muscle using a surface electrode array. Short static tests of 3.8 s (41 subjects) and prolonged dynamic tests (prolonged tests) of 4 min (30 subjects) were performed as position tasks, applying forces up to 20% of maximal voluntary contraction (MVC). Four variables, derived from the inter-peak latency technique, were used to describe changes in the surface electromyography signal: the mean muscle fibre conduction velocity (CV), the proportion between slow and fast MUPs expressed as the within-subject skewness of MUP velocities, the within-subject standard deviation of MUP velocities [SD-peak velocity (PV)], and the amount of MUPs per second (peak frequency = PF). In short static tests and the initial phase of prolonged tests, larger forces induced an increase of the CV and PF, accompanied with the shift of MUP velocities towards higher values, whereas the SD-PV did not change. During the first 1.5–2 min of the prolonged lower force levels tests (unloaded, and loaded 5 and 10% MVC) the CV and SD-PV slightly decreased and the MUP velocities shifted towards lower values; then the three variables stabilized. The PF values did not change in these tests. However, during the prolonged higher force (20% MVC) test, the CV decreased and MUP velocities shifted towards lower values without stabilization, while the SD-PV broadened and the PF decreased progressively. It is argued that these combined results reflect changes in both neural regulatory strategies and muscle membrane state

Sarcolemmal membrane excitability during repeated intermittent maximal voluntary contractions.

What is the central question of this study? Is impaired membrane excitability reflected by an increase or by a decrease in M-wave amplitude? What is the main finding and its importance? The magnitude of the M-wave first and second phases changed in completely different ways during intermittent maximal voluntary contractions, leading to the counterintuitive conclusion that it is an increase (and not a decrease) of the M-wave first phase that reflects impaired membrane excitability. The study was undertaken to investigate separately the changes in the first and second phases of the muscle compound action potential (M-wave) during 4 min of intermittent maximal voluntary contractions (MVCs) of the quadriceps. M-waves were evoked by supramaximal single electrical stimulation to the femoral nerve delivered in the resting periods between 48 successive MVCs of 3 s. The amplitude, duration and area of the M-wave first and second phases were measured separately, together with muscle conduction velocity and MVC force. During the intermittent MVCs, the amplitude of the M-wave first phase increased uninterruptedly for the first 3 min (12-16%, P < 0.05) and stabilized thereafter, whereas the second phase initially increased for 55-75 s (11-22%, P < 0.05), but decreased subsequently. The enlargement of the first phase occurred in parallel with an increase in its duration, and concomitantly with a decline in conduction velocity (maximal cross-correlations, 0.89-0.97; time lag, 0 s). Also, a significant temporal association was found between the amplitude of the first phase and MVC force (time lag, 0 s; maximal cross-correlations, 0.85-0.97). Conversely, there was no temporal association between the second phase amplitude and conduction velocity or MVC force (time lag, 73-117 s; maximal cross-correlations, 0.65-0.77). It is concluded that the enlargement of the M-wave first phase is the electrical manifestation of impaired muscle membrane excitability. The results highlight the importance of independently analysing the first and second phases, as only the first phase can be used reliably to detect changes in membrane excitability, while the second might be affected by muscle architecture