Non-invasive approach of motor unit recording during muscle contractions in humans

Abstract

Information about the structural and functional characteristics of the motor unit (MU) is highly relevant for the diagnosis of neuromuscular disorders. Electromyography (EMG) is a suitable method for obtaining the information needed. The problem is the separation of the activity of one MU from others which are simultaneously active. Such investigations of single MU activity have commonly used invasive methods, e.g. employing a needle or a wire. Conventional surface-EMG methods have limited resolution and detect, at high contraction levels, multiple MU superimposed one on the other. The separation of the activity of a single MU can be achieved in a non-invasive way when highly specialised acquisition techniques are used. One approach, called high spatial resolution EMG (HSR-EMG), is based on the use of multi-electrode arrays in combination with a two-dimensional Laplace filter. The HSR-EMG permits the completely non-invasive detection of single MU activity even during maximal voluntary contractions. First applications have shown that the method provides a deeper insight into the functional and structural characteristics of the MU. In this paper the application of HSR-EMG to the diagnosis of neuromuscular disorders will be presented, and the latest results will be given of its application in the evaluation of treatment of patients with plexus lesion.

Fingertip force estimation from forearm muscle electrical activity

“…By skipping increasingly more rows, filters were formed for L=5 and 4 channels. Next, these four row selections were repeated, utilizing additional adjacent columns to form bipolar and linear double difference (LDD) filters [20]. Note that these filters were formed along the presumed direction of action potential propagation.…”

“…In particular, the muscles of the forearm are small in cross section and packed tightly beside each other, making it difficult to sense their activities independently. Over the past few years, high resolution spatial filtering of EMG array signals has been used to localize the electrical potentials of small muscle tissue volumes [18][19][20]. We hypothesized that such systems would be useful in separating the source electrical activity of distinct hand muscles within the forearm, facilitating more accurate EMG-force identification.…”

Single motor unit analysis from spatially filtered surface electromyogram signals. Part I: Spatial selectivity

“…Spatial filtering methods were shown to provide a high enough selectivity to separate single MUAPs from the interference EMG signal, even at relatively high force contraction levels (DISSELHORST-KLUG et al, 1998;2000;RAU et al, 1997;REUCHER et al, 1987b). Many spatial filters have been proposed in the past and applied in basic and clinical studies (DISSELHORST-KLUG et al, 1998;FARINA and CESCON, 2001;FARINA and RAINOLDI, 1999;HOGREL AND DUCHI~NE, 1999;HUPPERTZ et al, 1997;RAMAEKERS et al, 1993).…”

EMG-to-force modeling for multiple fingers

“…Formation of Classic Spatial Filters: Classic spatially filtered channels, using known (pre-selected) spatial filter weights [4], were formed. A spatial filter is a memory-less weighted sum of the monopolar signals.…”