| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
1Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark; 2Laboratory for Engineering of the Neuromuscular System, Department of Electronics, Politecnico di Torino, Torino, Italy; and 3Department of Integrative Physiology, University of Colorado at Boulder, Boulder, Colorado
Submitted 1 February 2008; accepted in final form 6 May 2008
 |
ABSTRACT |
|---|
|
|
|
INTRODUCTION |
|---|
|
), attempts to identify the discharge of individual motor units in the surface electromyogram (EMG) have been a more recent development (De Luca et al. 2006; Gazzoni et al. 2004; Hogrel 2003; Holobar and Zazula 2004; Kleine et al. 2000, 2007, 2008; Zazula and Holobar 2005). The advantages of using surface EMG recordings are that the technique is noninvasive and it may be able to detect a greater number of motor units than is currently possible with intramuscular recordings (Holobar et al. 2006).
One of the challenges with the surface EMG approach is that the low-pass filtering effect of the tissues interposed between the fibers and the electrodes causes surface-detected potentials of different motor units to have a similar shape (Dimitrov and Dimitrova 1974; Merletti et al. 1999; Stegeman et al. 2000). However, the recording of the surface EMG with multiple electrodes, which are known as multichannel systems, may enhance the capacity to discriminate the action potentials of separate motor units (Blok et al. 2002; Zwarts et al. 2004). This is accomplished by multichannel systems providing many recordings of motor-unit activity along the length of a muscle (Masuda et al. 1985) or over its surface area (Blok et al. 2002), which facilitates discrimination of the action potentials belonging to different motor units.
The improved discrimination capacity of multichannel systems compared with the classic bipolar recording of the surface EMG has been shown for one subject using an EMG decomposition algorithm (Kleine et al. 2007). Under some conditions, however, it is possible to discriminate motor-unit action potentials with only a few surface EMG channels (De Luca et al. 2006). Despite demonstrations that specific algorithms can decompose representative recordings (De Luca et al. 2006; Gazzoni et al. 2004; Holobar and Zazula 2004; Kleine et al. 2007), the capacity of surface EMG recordings to discriminate motor-unit activity in a variety of conditions, as can be achieved with intramuscular EMG recordings, remains debatable.
Single motor-unit activity can be identified in surface EMG recordings only when the motor units are uniquely represented by their surface action potentials. This condition does not depend on the algorithm used to decompose the surface EMG or on the percentage of superimposed action potentials, but is related to the intrinsic information content of the surface EMG. If surface action potentials of individual motor units in a population all differ from each other, it should be possible to discriminate the discharge activity of each motor unit in the surface EMG recordings. The current study did not examine the capacity of specific decomposition algorithms (e.g., Kleine et al. 2007) to identify single motor units in the surface EMG, but rather we investigated the conditions that allow the discrimination of motor units on the basis of their surface EMG representations. When these conditions are not met, the action potentials of different motor units cannot be discriminated by any decomposition algorithm. The aim of the study was to investigate the relative proportion of motor units that are uniquely represented in the surface EMG detected with selected recording systems. The results provide the upper limit for detecting single motor units from the surface EMG. The ultimate success of any surface EMG decomposition procedure depends on the accuracy of the specific algorithm implemented, which is not addressed in this study.
|
|
METHODS |
|---|
|
) with an anisotropic muscle layer, and isotropic bone, subcutaneous, and skin layers (Fig. 1). The model is similar to one used in a previous study (Keenan et al. 2005) and the parameters are reported in Table 1.
|
|
. Average fiber length was the same for all motor units and varied between 60 mm (short muscle) and 120 mm (long muscle). Fibers were parallel to each other and along the main axis of the cylindrical volume conductor (Keenan et al. 2005). The innervation zones of the motor units (n = 200) were located at –20 mm (–10 mm for the short muscle), 0 mm, or 20 mm (10 mm) from the center of the muscle (Fig. 1B). The locations of the end plates and tendon endings varied randomly (uniform distribution) over a range of 5 mm. The motor-unit territories were circular and distributed randomly throughout the muscle. The fibers of each motor unit were distributed with a density of 20 fibers/mm2 (Armstrong et al. 1988) and interdigitated with the fibers of many other units to yield a fiber density in the muscle of 200 fibers/mm2. When a portion of the motor-unit territory was constrained by the muscle boundary, the territory of the unit was modified to fit the muscle cross section (Keenan et al. 2005). Innervation numbers ranged from 15 to 1,500, based on an approximately 100-fold range of twitch forces (Elek et al. 1992), with an exponential distribution across the population (Fuglevand et al. 1993; Kernell 1992).
The motor units had a mean muscle fiber conduction velocity of 4.0 ± 0.35 m/s (range 3.2–5.0 m/s) (Farina et al. 2000; Troni et al. 1983), with the slowest conduction velocity assigned to the smallest motor unit (Andreassen and Arendt-Nielsen 1987). The surface-recorded motor-unit potential constituted the sum of the action potentials of the muscle fibers belonging to the motor unit. EMG signals were computed at 4,096 samples/s. Each simulated motor-unit population comprised 200 motor units. The action potentials for each motor unit were generated independently, without activating the other motor units. The action potentials generated by pairs of the 200 motor units in each population were compared.
Simulated recording systems
The simulated signals were detected with circular electrodes (diameter 1 mm), arranged in a grid with 11 rows and 11 columns (11 x 11 electrodes) with either 2.5 mm (short muscle) or 5 mm (long muscle) between electrodes in both the longitudinal and transverse directions (Fig. 1B). The center of the grid was either 7.5 mm (short muscle) or 15 mm (long muscle) distal to the center of the muscle in the longitudinal direction and placed over the center of the muscle in the transverse direction. Each simulated signal (channel) represented the filtered version of the electric potential generated by the motor unit at one of the locations over the skin. The simulated filters were monopolar, bipolar, double differential, and Laplacian (Fig. 1) for comparison with commonly used experimental measures (Hogrel 2003). In addition, the four-bipolar configuration (quadrupolar), developed by De Luca et al. (2006) to decompose the surface EMG, was also examined (Fig. 1C); this configuration provides four channels.
The channels were grouped in sets along the transverse and longitudinal directions to obtain different recording configurations with varying numbers of channels that could be used to discriminate motor-unit action potentials. Figure 2 shows examples of channel groupings for the Laplacian filter: one filter produces one channel of information and one recording (Fig. 2A), three filters aligned in a transverse direction provide three channels and three recordings (Fig. 2B), three filters arranged in a longitudinal direction also provide three channels and three recordings (Fig. 2C), and the combination of three filters in each of the transverse and longitudinal directions obtains nine channels and nine recordings (Fig. 2D). Similar configurations in the two directions were also created for 4 channels (2 rows and 2 columns), 25 channels (5 and 5), 49 channels (7 and 7), and 81 channels (9 and 9). These configurations will be indicated as arrays of 2 x 2, 3 x 3, 5 x 5, 7 x 7, and 9 x 9 channels. Each group of channels was arranged around the center of the 11 x 11 grid, as shown in Fig. 2, with a possible misalignment of one interelectrode distance in the case of even number of channels.
|
Simulation conditions and signal analysis
The simulated electrical activity of each motor unit consisted of a multichannel action potential derived from the set of action potentials recorded at each location. The multichannel action potentials of the 200 motor units were simulated as detected at the skin surface for each recording configuration and anatomical condition. The three anatomical conditions simulated were the length of the fibers (60 and 120 mm), the thickness of the subcutaneous tissue (1 and 5 mm), and the locations of the innervation zones. The latter condition involved all motor units being innervated in one of the three simulated locations and each motor unit being innervated randomly in one of the three locations.
Twenty populations of 200 motor units were simulated by randomly locating the motor units within the muscle tissue for each of the 12 anatomical conditions (2 fiber lengths, 2 subcutaneous thicknesses, 3 distributions of innervation zones). The action potentials of the 200 motor units in each of the 240 simulated populations of motor units (12 anatomies x 20 random distributions) were detected by 89 recording configurations (22 configurations of channels in the longitudinal, transverse, and combined directions x 4 filters, plus the quadrupolar configuration). Due to the amount of data generated for this study, only representative results are reported herein.
Pairs (n = 19,900) of simulated motor units in each population of 200 motor units were compared with every other motor unit in the population. The two multichannel action potentials for each pair of motor units were aligned in time by maximizing their cross-correlation function. The mean square difference was computed between the two aligned multichannel action potentials and was normalized (%) to the mean of the energies of the two action potentials. Pairs of action potentials with a mean square difference of <5% were considered identical and the two motor units were deemed indistinguishable. The 5% criterion was based on the variability observed experimentally in the energy of surface action potentials discharged by the same motor unit in the abductor digiti minimi, which was the muscle experimentally investigated in this study. The signals used to compute the variability were taken from Farina et al. (2004b); the variability in the shape of the surface action potential was about 6% of the energy of the signal for individual motor units. This resolution is necessary to distinguish the action potentials of a single motor unit from other units in the surface recording. Two motor units can be discriminated from each other if the difference in the action potential shapes is larger than the variability in the shape of the action potentials of each of the two motor units. The dependent variable was thus the number of motor units with multichannel action potentials that differed from all other motor units in the population. This number depended on the configuration used to record the action potentials. The approach does not depend on the number of superimposed action potentials in a recording; rather, the intent was to assess the upper limit in the number of motor units that could be discriminated based on their surface EMG representation.
In each condition, the results are reported as mean and SD for 20 motor-unit populations with the same recording system and anatomical characteristics, but with different random locations of the motor units within the muscle.
Experimental measurements
Experimental signals were recorded from the abductor digiti minimi muscle of eight healthy men (mean ± SD, 26.1 ± 3.2 yr) with 49 circular electrodes (diameter 1 mm) that were arranged in a grid (7 x 7 electrodes) with 2.5 mm between electrodes in both the longitudinal and the transverse directions. The study was conducted in accordance with the Declaration of Helsinki, approved by the local ethics committee (N-20070019), and written informed consent was obtained from all subjects prior to participating in the study. Ultrasound recordings (FFsonic UF-4000L, Fukuda Denshi) indicated that the thickness of the subcutaneous tissue over the abductor digiti minimi for the eight subjects was 1.7 ± 0.5 mm.
The electrode grid was placed over the distal portion of the muscle after light abrasion of the skin. The monopolar surface EMG signals were amplified (64-channel surface EMG amplifier, SEA 64, LISiN-OT Bioelettronica, Turin, Italy; –3 dB bandwidth 10–500 Hz), sampled at 2,048 Hz, and converted to digital form by a 12-bit A/D converter.
Motor units were identified from intramuscular recordings and the corresponding surface action potentials were extracted by spike-triggered averaging (Farina et al. 2002). To detect a relatively large number of motor units from each subject, the intramuscular EMG signals were recorded concurrently from two locations and during contractions at five target forces. Intramuscular EMG signals were recorded with two pairs of Teflon-coated stainless steel wires (diameter 0.1 mm; A-M Systems, Carlsborg, WA) inserted with 25-gauge hypodermic needles into two locations that were about 10 mm apart in the transverse direction in the proximal part of the muscle. The needles were inserted to a depth of a few millimeters below the muscle fascia and removed to leave the wire electrodes inside the muscle. The wires were cut to expose the cross section and the intramuscular EMG signals were differentially amplified (Counterpoint EMG, Dantec Medical, Skovlunde, Denmark), band-pass filtered (500 Hz to 5 kHz), sampled at 10 kHz, and stored after 12-bit A/D conversion. The gain of the intramuscular EMG amplifier was adjusted to maximize the amplitude of the action potentials at each contraction force. The position of the wires was slightly adjusted before the recordings and in a few cases the wires were reinserted when the signal-to-noise ratio was judged poor from visual inspection. Once the optimal location was determined, however, the position of the wires was not changed between contractions. Surface and intramuscular recordings were synchronized.
The fifth finger was fixed in a brace to record the force exerted during an isometric contraction of the muscle (Politecnico di Turin, Turin, Italy). The subjects performed three maximal voluntary contractions (MVCs) of the abductor digiti minimi with a 2-min rest between each MVC. The peak MVC force was used as the reference for the submaximal contraction forces. Five minutes after the MVCs, the subject performed five 30-s contractions at target forces of 2.5, 5, 7.5, 10, and 12.5% MVC force; there was a 5-min rest between each 30-s contraction.
The action potentials of the detected motor units were identified from the intramuscular recordings with a decomposition algorithm (McGill et al. 2005). Accuracy of the automatic part of this algorithm is >95% compared with expert manual decomposition (JR Florestal, PA Mathieu, and KC McGill, unpublished observations). Moreover, the algorithm includes a user interface for manually editing and verifying the results. The software displays a segment of the EMG signal, the templates of the action potentials of the identified motor units, the discharge patterns, and a close-up of the signal for resolving missed discharges and superimpositions. The automatic decomposition was checked by inspection of the identified discharge patterns. Full, regular patterns provided confidence that the decomposition was correct, whereas gaps, extra discharges, or uneven intervals indicated possible decomposition errors. To assist in identifying missed discharges, the program displays bars in the signal panel that indicate the expected discharge times of each motor unit. The intramuscular decomposition procedure has been validated (Florestal et al., unpublished observations).
The action potentials discharged by each identified motor unit were used to trigger an average from the multichannel surface EMG (Farina et al. 2002) to obtain isolated surface EMG action potentials for each motor unit, as in the simulations. Surface-recorded action potentials generated by the same motor unit at different target forces or concurrently at the two intramuscular locations were averaged. The identification of the same motor unit at different force levels was based on the shape of the intramuscular action potentials, which was the reference for the identification of a unique motor-unit action potential. Because the motor units were recorded at varying forces, however, it cannot be fully excluded that in some cases the same motor unit was included two or more times in the experimental population when detected at different forces. The shape of the intramuscular action potentials may have changed slightly with contraction force due to displacement of the wires. Thus the experimental population of motor units defined as unique on the basis of intramuscular recordings at different forces may have contained a few cases of nonunique motor units (see DISCUSSION).
The surface multichannel action potentials of the identified motor units, as obtained by spike-triggered averaging, were compared with the same procedures used with the simulated motor units and the same configurations of channels (Fig. 2), up to a maximum of five channels in each direction due to the smaller size of the electrode grid used experimentally with respect to the simulated grid. Results are reported for each subject as the percentage of identifiable motor units for representative recording configurations.
|
|
RESULTS |
|---|
|
|
|
). The percentages of identifiable motor units in the long muscle for all recording configurations when the motor-unit populations had the same location for the center of the innervation zone and 5 mm of subcutaneous tissue are reported in Table 2. Similar results were obtained for populations of motor units with different innervation-zone locations and only 1 mm of subcutaneous tissue. However, thinner subcutaneous tissue enabled the identification of larger percentages of motor units for monopolar, bipolar, and double differential filtering. For example, the configuration of 81 channels arranged in the two directions (9 x 9 channels) with bipolar filtering could discriminate 57.0 ± 3.6% of the motor units when the subcutaneous tissue was 1 mm thick in the same conditions as in Table 2.
|
Variation in the location of the center of the innervation zone for each motor unit improved the discrimination capacity for some recording configurations. The locations of the innervation zone were randomly varied among three positions: –20 mm (–10 mm for the short muscle), 0 mm, and 20 mm (10 mm for the short muscle) from the muscle center (Fig. 1). The greatest improvement in performance occurred for the estimates derived from bipolar filtering (Fig. 4, C and D). When the subcutaneous layer was 1 mm thick and the innervation zone locations were scattered in the long muscle, the configuration of 81 channels (9 x 9) with bipolar filtering identified a similar percentage of motor units as the same configuration with Laplacian filtering (Fig. 4D). Similar results were obtained for the short muscle, where the configuration of 9 x 9 bipolar channels identified 76.9 ± 4.0% of the motor units when the innervation zones were scattered and the subcutaneous layer was 1 mm thick.
Because low-threshold units are usually analyzed in experimental studies, the percentage of identifiable motor units was further analyzed as a function of motor-unit territory. There was a tendency for motor units with small territories to be better identified than larger units (results are shown for the long muscle in Fig. 5A). However, this effect was absent for motor units that innervated >100 fibers. There was no electrode configuration that was able to discriminate all motor units in a specific range of recruitment thresholds (e.g., low-threshold motor units) when using few channels. The motor units that could not be distinguished tended to have a similar size and location (Fig. 3); these motor units would be activated at similar forces during voluntary contractions. Results on the short muscle were comparable with those for the long muscle.
|
|
|
|
|
|
|
DISCUSSION |
|---|
|
Due to the low-pass filtering effects of the tissues interposed between the active muscle fibers and the electrodes, surface-detected action potentials for different motor units can be similar and thus indistinguishable (Fig. 3). Under some conditions, it is possible to identify motor-unit potentials in the surface EMG (De Luca et al. 2006; Gazzoni et al. 2004; Kleine et al. 2007). However, there has been no theoretical evaluation of the use of surface EMG recordings to study motor-unit activity. The approach used in the present study was to determine the conditions that enabled the appearance of unique motor-unit potentials in the simulated surface EMG recording and to verify the outcomes with experimental recordings. The analysis does not depend on the decomposition method because the signal cannot be decomposed if it does not contain motor-unit potentials with unique shapes. The findings in the current study thus indicate the upper limit in number of units that can be discriminated from the surface EMG. In practice, the number of motor units and action potential discharges that can be identified will depend on the ability of the decomposition algorithm to detect and classify action potentials and on the degree of complexity of the signal (e.g., percentage of superimposed action potentials), which has not been addressed in this study.
The simulation and experimental results show that when only a few channels of surface EMG recordings are used in the discrimination, it is not possible to discriminate surface motor-unit potentials. The information content of a few surface EMG channels is thus not sufficient for investigating the activity of single motor units. On average, only 33.7% of the low-threshold motor units recorded experimentally could be distinguished with one bipolar channel. In some special cases, decomposition of the surface EMG from few recorded channels is possible (De Luca et al. 2006; Hogrel 2003), but the results do not generalize and the methods have only limited applicability. The poor discrimination performance of a few channels of recordings applies to motor units with different sizes and at different distances from the recording electrodes. For example, when limiting the motor units of interest to those within 10 mm of the electrodes (because the others may have action potentials below the noise level), the percentage of simulated motor units that can be discriminated with a single bipolar or Laplacian channel is <20% (Fig. 5C), which is consistent with the percentage of identifiable motor units observed in the experimental recordings. Furthermore, <60% of the simulated motor units could be discriminated under the same conditions with the system of four channels proposed by De Luca et al. (2006) (Fig. 5C). Many of the motor units that cannot be identified are superficial and small (Fig. 7), which are those most likely to be recorded during voluntary contractions in motor-unit studies. These indistinguishable motor units cannot be discriminated by any decomposition algorithm because they appear as a single source.
The number of identifiable motor units in the simulated signals increased substantially when there was an increase in the number of channels used in the discrimination process. A similar result was obtained with the experimental measurements, which showed that about 80% of the detected motor units could be distinguished with a multichannel system. In experimental conditions, the shape of the intramuscular action potentials was used to ensure that the motor units detected for each subject represented a population of unique units. However, the experimental recordings were performed at various forces to identify lower-threshold motor units, which are usually masked by larger action potentials at higher forces. At each force level, all motor units that contributed to the signal were identified by automatic and interactive decomposition. The motor units detected at different forces were compared and merged when the same motor unit was identified. Although this procedure involved visual inspection of the shapes of the intramuscular action potentials and computation of their mean square errors, it cannot be excluded that some units were included more than once in the experimental population due to a change in the shape of their intramuscular action potential shapes. The number of motor units reported in Table 3 may overestimate the actual number of individual motor units detected experimentally.
Although the averaging was performed with all available triggers, it is possible that the variability in the shape of the averaged surface potentials was >5%, which was the level assumed in the simulations. The larger variability in surface potentials may have decreased the capacity to discriminate among motor units. The potential overestimation in the number of unique motor units based on intramuscular recordings and the potential larger variability in surface EMG action potential shapes due to spike-triggered averaging with a finite number of triggers indicate that the percentage of motor units that could be discriminated from experimental recordings as reported in Table 3 may have been underestimated. Thus the data in Table 3 likely provide a conservative estimate of the number of motor unit action potentials that can be discriminated experimentally based on surface EMG. These problems would have a similar influence on all channel configurations and thus would not alter the general conclusions.
The results indicate that large electrode grids are necessary to discriminate a high proportion of the motor-unit population from noninvasive surface EMG recordings (Kleine et al. 2007). Contrary to intramuscular recordings, it is relatively easy to increase the number of recording locations when applying surface electrodes. High-density surface EMG systems have been developed in several research laboratories (Blok et al. 2002; Gazzoni et al. 2004; Holtermann et al. 2005; Kleine et al. 2007; Lapatki et al. 2006; Madeleine et al. 2006) with construction techniques that allow for easy placement of the grid over a muscle of interest. Although the current study shows that motor units can be discriminated in surface EMG recordings under some conditions, the work did not consider the development of algorithms that could detect and discriminate motor unit action potentials in the interference EMG.
The ability to distinguish motor unit action potentials was influenced by the recording configuration and the anatomical characteristics of the system. The discrimination capacity of Laplacian filtering is greater than that of monopolar recordings (Table 2) due to the effect that differentiation has on enhancing the differences between action potentials generated by motor units at different locations in the muscle. The ability to identify individual motor-unit potentials was also influenced by selected anatomical conditions, such as the thickness of the subcutaneous layer and the location of the innervation zones. Although the simulations reported in this study varied only a few anatomical parameters, similar trends were also observed for other simulation conditions (data not reported). For example, the conclusions were not influenced by the muscle cross-sectional area or number of motor units. Similarly, the uniform distribution of innervation number across the motor-unit pool, instead of the exponential distribution reported in this study, led to almost identical results. Thus although the present report focuses on only a subset of results, all conclusions were confirmed in a larger set of simulations.
Although the simulation results are limited by the relatively simple geometry of the volume conductor, more complex geometries would not have substantially changed the results. For example, simulation of fiber pennation (Mesin and Farina 2004) and tissue inhomogeneities (Mesin and Farina 2005) had an effect on action potential amplitude but not on the shape of the motor unit action potentials. The influences of fiber pennation and tissue inhomogeneities on action potential amplitude were similar across motor units, spatial filters, and multichannel configurations (Mesin and Farina 2004, 2005).
In summary, the simulation data and the experimental recordings indicate that relatively few motor units are distinguishable when only few channels of surface EMG signals are available for discrimination. In contrast, currently available multichannel, surface EMG recordings can discriminate a high proportion of the motor units in a simulated population and thus theoretically permit single motor-unit analysis. The extraction of motor-unit activity from surface EMG signals thus requires the use of electrode grids and not simpler detection systems that comprise only a few channels of recordings.
|
|
GRANTS |
|---|
|
|
|
FOOTNOTES |
|---|
Address for reprint requests and other correspondence: R. M. Enoka, University of Colorado, Department of Integrative Physiology, Boulder, CO 80309-0354 (E-mail: enoka{at}colorado.edu)
|
|
REFERENCES |
|---|
|
Andreassen S, Arendt-Nielsen L. Muscle fibre conduction velocity in motor units of the human anterior tibial muscle: a new size principle parameter. J Physiol 391: 561–571, 1987.
Armstrong JB, Rose PK, Vanner S, Bakker GJ, Richmond FJ. Compartmentalization of motor units in the cat neck muscle, biventer cervicis. J Neurophysiol 60: 30–45, 1988.
Blok JH, van Dijk JP, Drost G, Zwarts MJ, Stegeman DF. A high-density multichannel surface electromyography system for the characterization of single motor units. Rev Sci Instrum 73: 1887–1897, 2002.[CrossRef][Web of Science]
De Luca CJ, Adam A, Wotiz R, Gilmore LD, Nawab SH. Decomposition of surface EMG signals. J Neurophysiol 96: 1646–1657, 2006.
Dimitrov GV, Dimitrova NA. Extracellular potential field of a single striated muscle fibre immersed in anisotropic volume conductor. Electromyogr Clin Neurophysiol 14: 423–436, 1974.[Medline]
Elek JM, Kossev A, Dengler R, Schubert M, Wohlfahrt K, Wolf W. Parameters of human motor unit twitches obtained by intramuscular microstimulation. Neuromuscul Disord 2: 261–267, 1992.[CrossRef][Medline]
Farina D, Arendt-Nielsen L, Merletti R, Graven-Nielsen T. Assessment of single motor unit conduction velocity during sustained contractions of the tibialis anterior muscle with advanced spike triggered averaging. J Neurosci Methods 115: 1–12, 2002.[CrossRef][Web of Science][Medline]
Farina D, Fortunato E, Merletti R. Noninvasive estimation of motor unit conduction velocity distribution using linear electrode arrays. IEEE Trans Biomed Eng 47: 380–388, 2000.[CrossRef][Web of Science][Medline]
Farina D, Gazzoni M, Camelia F. Low-threshold motor unit membrane properties vary with contraction intensity during sustained activation with surface EMG visual feedback. J Appl Physiol 96: 1505–1515, 2004b.
Farina D, Mesin L, Martina S, Merletti R. A surface EMG generation model with multilayer cylindrical description of the volume conductor. IEEE Trans Biomed Eng 51: 415–426, 2004a.[CrossRef][Web of Science][Medline]
Fuglevand AJ, Winter DA, Patla AE. Models of recruitment and rate coding organization in motor-unit pools. J Neurophysiol 70: 2470–2488, 1993.
Gazzoni M, Farina D, Merletti R. A new method for the extraction and classification of single motor unit action potentials from surface EMG signals. J Neurosci Methods 136: 165–177, 2004.[CrossRef][Web of Science][Medline]
Hogrel JY. Use of surface EMG for studying motor unit recruitment during isometric linear force ramp. J Electromyogr Kinesiol 13: 417–423, 2003.[CrossRef][Web of Science][Medline]
Holobar A, Zazula D. Correlation-based decomposition of surface electromyograms at low contraction forces. Med Biol Eng Comput 42: 487–495, 2004.[CrossRef][Web of Science][Medline]
Holobar A, Zazula D, Gazzoni M, Merletti R, Farina D. Noninvasive analysis of motor unit discharge patterns in isometric force-varying contractions. Proc XVI Congr Int Soc Electrophysiol Kinesiol, June 28–July 1, Torino, Italy, 2006, p. 12.
Holtermann A, Roeleveld K, Karlsson JS. Inhomogeneities in muscle activation reveal motor unit recruitment. J Electromyogr Kinesiol 15: 131–137, 2005.[CrossRef][Web of Science][Medline]
Keenan KG, Farina D, Maluf KS, Merletti R, Enoka RM. Influence of amplitude cancellation on the simulated surface electromyogram. J Appl Physiol 98: 120–131, 2005.
Kernell D. Organized variability in the neuromuscular system: a survey of task-related adaptations. Arch Ital Biol 130: 19–66, 1992.[Web of Science][Medline]
Kleine BU, Blok JH, Oostenveld R, Praamstra P, Stegeman DF. Magnetic stimulation-induced modulations of motor unit firings extracted from multi-channel surface EMG. Muscle Nerve 23: 1005–1015, 2000.[CrossRef][Web of Science][Medline]
Kleine BU, van Dijk JP, Lapatki BG, Zwarts MJ, Stegeman DF. Using two-dimensional spatial information in decomposition of surface EMG signals. J Electromyogr Kinesiol 17: 535–548, 2007.[CrossRef][Web of Science][Medline]
Kleine BU, van Dijk JP, Zwarts MJ, Stegeman DF. Inter-operator agreement in decomposition of motor unit firings from high-density surface EMG. J Electromyogr Kinesiol 18: 652–661, 2008.[CrossRef][Web of Science][Medline]
Lapatki BG, Oostenveld R, Van Dijk JP, Jonas IE, Zwarts MJ, Stegeman DF. Topographical characteristics of motor units of the lower facial musculature revealed by means of high-density surface EMG. J Neurophysiol 95: 342–354, 2006.
Madeleine P, Leclerc F, Arendt-Nielsen L, Ravier P, Farina D. Experimental muscle pain changes the spatial distribution of upper trapezius muscle activity during sustained contraction. Clin Neurophysiol 117: 2436–2445, 2006.[CrossRef][Web of Science][Medline]
Masuda T, Miyano H, Sadoyama T. The position of innervation zones in the biceps brachii investigated by surface electromyography. IEEE Trans Biomed Eng 32: 36–42, 1985.[Web of Science][Medline]
McGill KC, Lateva ZC, Marateb HR. EMGLAB: an interactive EMG decomposition program. J Neurosci Methods 149: 121–133, 2005.[CrossRef][Web of Science][Medline]
Merletti R, Lo Conte L, Avignone E, Guglielminotti P. Modeling of surface myoelectric signals—Part I: Model implementation. IEEE Trans Biomed Eng 46: 810–820, 1999.[CrossRef][Web of Science][Medline]
Mesin L, Farina D. Simulation of surface EMG signals generated by muscle tissues with inhomogeneity due to fiber pinnation. IEEE Trans Biomed Eng 51: 1521–1529, 2004.[CrossRef][Web of Science][Medline]
Mesin L, Farina D. A model for surface EMG generation in volume conductors with spherical inhomogeneities. IEEE Trans Biomed Eng 52: 1984–1993, 2005.[CrossRef][Web of Science][Medline]
Reucher H, Silny J, Rau G. Spatial filtering of noninvasive multielectrode EMG: Part II—Filter performance in theory and modeling. IEEE Trans Biomed Eng 34: 106–113, 1987.[Web of Science][Medline]
Rosenfalck P. Intra- and extracellular potential fields of active nerve and muscle fibers. Acta Physiol Scand Suppl 47: 239–246, 1969.
Stegeman DF, Blok JH, Hermens HJ, Roeleveld K. Surface EMG models: properties and applications. J Electromyogr Kinesiol 10: 313–326, 2000.[CrossRef][Web of Science][Medline]
Troni W, Cantello R, Rainero I. Conduction velocity along human muscle fibers in situ. Neurology 33: 1453–1459, 1983.
Zazula D, Holobar A. An approach to surface EMG decomposition based on higher-order cumulants. Comput Methods Programs Biomed Suppl 1: S51–S60, 2005.
Zwarts MJ, Lapatki BG, Kleine BU, Stegeman DF. Surface EMG: how far can you go? Suppl Clin Neurophysiol 57: 111–119, 2004.[Medline]
This article has been cited by other articles:
|
|
 |
|
  D. Farina, A. Holobar, M. Gazzoni, D. Zazula, R. Merletti, and R. M. Enoka Adjustments Differ Among Low-Threshold Motor Units During Intermittent, Isometric Contractions J Neurophysiol, January 1, 2009; 101(1): 350 - 359. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Visit Other APS Journals Online |
TOP
ABSTRACT
INTRODUCTION
81 channels (9 x 9) in both directions. The motor-unit action potentials shown in this figure were obtained from a simulation with a subcutaneous layer that was 5 mm thick, the innervation zones located at –20 mm in the longitudinal direction from the center of the muscle, and fiber length of 120 mm. au, arbitrary units.
