A comparison of cross-correlation and surface EMG techniques used to quantify motor unit synchronization in humans

Synaptic inputs to motor neurons underlying muscle coactivation for functionally different tasks have different spectral characteristics

The central nervous system (CNS) may produce the same endpoint trajectory or torque profile with different muscle activation patterns. What differentiates these patterns is the presence of cocontraction, which does not contribute to effective torque generation but allows to modulate joints' mechanical stiffness. Although it has been suggested that the generation of force and the modulation of stiffness rely on separate pathways, a characterization of the differences between the synaptic inputs to motor neurons (MNs) underlying these tasks is still missing. In this study, participants coactivated the same pair of upper-limb muscles, i.e., the biceps brachii and the triceps brachii, to perform two functionally different tasks: limb stiffness modulation or endpoint force generation. Spike trains of MNs were identified through decomposition of high-density electromyograms (EMGs) collected from the two muscles. Cross-correlogram showed a higher synchronization between MNs recruited to modulate stiffness, whereas cross-muscle coherence analysis revealed peaks in the β-band, which is commonly ascribed to a cortical origin. These peaks did not appear during the coactivation for force generation, thus suggesting separate cortical inputs for stiffness modulation. Moreover, a within-muscle coherence analysis identified two subsets of MNs that were selectively recruited to generate force or regulate stiffness. This study is the first to highlight different characteristics, and probable different neural origins, of the synaptic inputs driving a pair of muscles under different functional conditions. We suggest that stiffness modulation is driven by cortical inputs that project to a separate set of MNs, supporting the existence of a separate pathway underlying the control of stiffness.NEW & NOTEWORTHY The characterization of the pathways underlying force generation or stiffness modulation are still unknown. In this study, we demonstrated that the common input to motor neurons of antagonist muscles shows a high-frequency component when muscles are coactivated to modulate stiffness but not to generate force. Our results provide novel insights on the neural strategies for the recruitment of multiple muscles by identifying specific spectral characteristics of the synaptic inputs underlying functionally different tasks.

Extraction of Multi-Labelled Movement Information from the Raw HD-sEMG Image with Time-Domain Depth

In contemporary muscle-computer interfaces for upper limb prosthetics there is often a trade-off between control robustness and range of executable movements. As a very low movement error rate is necessary in practical applications, this often results in a quite severe limitation of controllability; a problem growing ever more salient as the mechanical sophistication of multifunctional myoelectric prostheses continues to improve. A possible remedy for this could come from the use of multi-label machine learning methods, where complex movements can be expressed as the superposition of several simpler movements. Here, we investigate this claim by applying a multi-labeled classification scheme in the form of a deep convolutional neural network (CNN) to high density surface electromyography (HD-sEMG) recordings. We use 16 independent labels to model the movements of the hand and forearm state, representing its major degrees of freedom. By training the neural network on 16 × 8 sEMG image sequences 24 samples long with a sampling rate of 2048 Hz to detect these labels, we achieved a mean exact match rate of 78.7% and a mean Hamming loss of 2.9% across 14 healthy test subjects. With this, we demonstrate the feasibility of highly versatile and responsive sEMG control interfaces without loss of accuracy.

Dynamical Coordination of Hand Intrinsic Muscles for Precision Grip in Diabetes Mellitus

This study investigated the effects of diabetes mellitus (DM) on dynamical coordination of hand intrinsic muscles during precision grip. Precision grip was tested using a custom designed apparatus with stable and unstable loads, during which the surface electromyographic (sEMG) signals of the abductor pollicis brevis (APB) and first dorsal interosseous (FDI) were recorded simultaneously. Recurrence quantification analysis (RQA) was applied to quantify the dynamical structure of sEMG signals of the APB and FDI; and cross recurrence quantification analysis (CRQA) was used to assess the intermuscular coupling between the two intrinsic muscles. This study revealed that the DM altered the dynamical structure of muscle activation for the FDI and the dynamical intermuscular coordination between the APB and FDI during precision grip. A reinforced feedforward mechanism that compensates the loss of sensory feedbacks in DM may be responsible for the stronger intermuscular coupling between the APB and FDI muscles. Sensory deficits in DM remarkably decreased the capacity of online motor adjustment based on sensory feedback, rendering a lower adaptability to the uncertainty of environment. This study shed light on inherent dynamical properties underlying the intrinsic muscle activation and intermuscular coordination for precision grip and the effects of DM on hand sensorimotor function.

Altered Motor Unit Discharge Coherence in Paretic Muscles of Stroke Survivors

After a cerebral stroke, a series of changes at the supraspinal and spinal nervous system can alter the control of muscle activation, leading to persistent motor impairment. However, the relative contribution of these different levels of the nervous system to impaired muscle activation is not well understood. The coherence of motor unit (MU) spike trains is considered to partly reflect activities of higher level control, with different frequency band representing different levels of control. Accordingly, the objective of this study was to quantify the different sources of contribution to altered muscle activation. We examined the coherence of MU spike trains decomposed from surface electromyogram (sEMG) of the first dorsal interosseous muscle on both paretic and contralateral sides of 14 hemispheric stroke survivors. sEMG was obtained over a range of force contraction levels at 40, 50, and 60% of maximum voluntary contraction. Our results showed that MU coherence increased significantly in delta (1–4 Hz), alpha (8–12 Hz), and beta (15–30 Hz) bands on the affected side compared with the contralateral side, but was maintained at the same level in the gamma (30–60 Hz) band. In addition, no significant alteration was observed across medium–high force levels (40–60%). These results indicated that the common synaptic input to motor neurons increased on the paretic side, and the increased common input can originate from changes at multiple levels, including spinal and supraspinal levels following a stroke. All these changes can contribute to impaired activation of affected muscles in stroke survivors. Our findings also provide evidence regarding the different origins of impaired muscle activation poststroke.

Correlation varies with different time lags between the motions of the hyoid bone, epiglottis, and larynx during swallowing

Although coordination and timing of swallowing have often been investigated by using discrete timing events such as the onset, peak, and duration of specific motions, the sequence and duration of swallowing events cannot represent the coordination of the swallowing mechanism quantitatively. This study aimed to apply a cross-correlation analysis of the motions of the hyolaryngeal structures during swallowing as an objective method for measuring the coordination and timing of the motions. Forty healthy subjects swallowed 2 and 5 ml of diluted barium solution (35 %) and 5 ml of curd yogurt under videofluoroscopy. Hyolaryngeal motions in videofluoroscopic images were digitized using the motion analysis system. The time series of the horizontal and vertical hyoid motion, the laryngeal elevation, and the angle of the epiglottic tilt were analyzed using cross-correlation at each 1/60-s time lag. The results showed high and consistent cross-correlations between hyolaryngeal motions during swallowing in most of the subjects regardless of age and bolus type. The horizontal hyoid motion and laryngeal elevation were more strongly correlated with the epiglottic tilt than the vertical hyoid motion, which might suggest the mechanism of the epiglottic tilt during swallowing. The bolus volume and viscosity affected the correlation coefficients and time lags between the hyolaryngeal motions, particularly those related to the epiglottic tilt. The results suggest that cross-correlation analysis may be used for measuring the coordination and timing of swallowing. Further studies using cross-correlation analysis of additional physiological factors related to swallowing or pathological conditions are warranted.

Pathological tremor prediction using surface electromyogram and acceleration: potential use in 'ON-OFF' demand driven deep brain stimulator design

OBJECTIVE

We present a proof of concept for a novel method of predicting the onset of pathological tremor using non-invasively measured surface electromyogram (sEMG) and acceleration from tremor-affected extremities of patients with Parkinson's disease (PD) and essential tremor (ET).

APPROACH

The tremor prediction algorithm uses a set of spectral (Fourier and wavelet) and nonlinear time series (entropy and recurrence rate) parameters extracted from the non-invasively recorded sEMG and acceleration signals.

MAIN RESULTS

The resulting algorithm is shown to successfully predict tremor onset for all 91 trials recorded in 4 PD patients and for all 91 trials recorded in 4 ET patients. The predictor achieves a 100% sensitivity for all trials considered, along with an overall accuracy of 85.7% for all ET trials and 80.2% for all PD trials. By using a Pearson's chi-square test, the prediction results are shown to significantly differ from a random prediction outcome.

SIGNIFICANCE

The tremor prediction algorithm can be potentially used for designing the next generation of non-invasive closed-loop predictive ON-OFF controllers for deep brain stimulation (DBS), used for suppressing pathological tremor in such patients. Such a system is based on alternating ON and OFF DBS periods, an incoming tremor being predicted during the time intervals when DBS is OFF, so as to turn DBS back ON. The prediction should be a few seconds before tremor re-appears so that the patient is tremor-free for the entire DBS ON-OFF cycle and the tremor-free DBS OFF interval should be maximized in order to minimize the current injected in the brain and battery usage.

Disrupted small-world brain network in children with Down Syndrome

OBJECTIVE

To explore how the global organization or topology of the functional brain connectivity (FBC) is affected in Down Syndrome (DS).

METHODS

As the brain is a highly complex network including numerous nonlinearly interacted neuronal areas, the FBCs of typically developing (TD) children and DS patients were computed using a nonlinear synchronization method. Then the differences in global organization of the obtained FBCs of the two groups were analyzed, in all electroencephalogram (EEG) frequency bands, in the framework of Small-Worldness Network (a network with optimum balance between segregation and integration of information).

RESULTS

The topology of the functional connectivity of DS patients is disrupted in the whole brain in alpha and theta bands, and especially in the left intra-hemispheric brain networks in upper alpha band.

CONCLUSIONS

The global organization of the DS brain does not resemble a Small-World network, but it works as a random network.

SIGNIFICANCE

It is the first study on global organization of the FBC in DS.

Characterization of spatiotemporally complex gait patterns using cross-correlation signatures

We hypothesize that spatiotemporal joint coupling patterns during gait are closely associated with musculoskeletal injury mechanics. Previous studies examining joint coupling, have primarily focused on coupling between single pairs of neighboring body segments or joints; thus falling short of characterizing the full spatiotemporal complexity across the entire gait apparatus. This study proposes the reliance on properties of the temporal cross-correlation of distinct joint variables as a means to characterize and detect differences in multiple segmental coupling pairs and to quantify how these couplings change between different gait conditions or test groups. In particular, for each subject, a characteristic diagram array is obtained whose entries include the maximum values of the cross-correlation between all pairs of joint variables as well as the associated phase shifts at which these maxima are recorded. Paired t-tests are then used to highlight significant differences in the corresponding entries between two gait conditions. In the present study, this technique was applied to angular displacement and velocity histories across 12 lower extremity joint variables, for healthy subjects with and without a brace on the right knee. As expected, the statistical analysis indicated that the temporal cross-correlations associated with the right knee-angle variables differed the most between the two gait conditions. In addition, significant differences (p<0.01) were found in the coupling between other pairs of joint variables, establishing a characteristic spatiotemporal signature for the changes from normative gait that result from reduced mobility at the knee.

Motor unit recruitment strategies and muscle properties determine the influence of synaptic noise on force steadiness

Motoneurons receive synaptic inputs from tens of thousands of connections that cause membrane potential to fluctuate continuously (synaptic noise), which introduces variability in discharge times of action potentials. We hypothesized that the influence of synaptic noise on force steadiness during voluntary contractions is limited to low muscle forces. The hypothesis was examined with an analytical description of transduction of motor unit spike trains into muscle force, a computational model of motor unit recruitment and rate coding, and experimental analysis of interspike interval variability during steady contractions with the abductor digiti minimi muscle. Simulations varied contraction force, level of synaptic noise, size of motor unit population, recruitment range, twitch contraction times, and level of motor unit short-term synchronization. Consistent with the analytical derivations, simulations and experimental data showed that force variability at target forces above a threshold was primarily due to low-frequency oscillations in neural drive, whereas the influence of synaptic noise was almost completely attenuated by two low-pass filters, one related to convolution of motoneuron spike trains with motor unit twitches (temporal summation) and the other attributable to summation of single motor unit forces (spatial summation). The threshold force above which synaptic noise ceased to influence force steadiness depended on recruitment range, size of motor unit population, and muscle contractile properties. This threshold was low (<10% of maximal force) for typical values of these parameters. Results indicate that motor unit recruitment and muscle properties of a typical muscle are tuned to limit the influence of synaptic noise on force steadiness to low forces and that the inability to produce a constant force during stronger contractions is mainly attributable to the common low-frequency oscillations in motoneuron discharge rates.

The pace of prosodic phrasing couples the listener's cortex to the reader's voice

We studied online coupling between a reader's voice and a listener's cortical activity using a novel, ecologically valid continuous listening paradigm. Whole-scalp magnetoencephalographic (MEG) signals were recorded from 10 right-handed, native French-speaking listeners in four conditions: a female (Exp1f) and a male (Exp1m) reading the same text in French; a male reading a text in Finnish (Exp 2), a language incomprehensible for the subjects, and a male humming Exp1 text (Exp 3). The fundamental frequency (f0) of the reader's voice was recorded with an accelerometer attached to the throat, and coherence was computed between f0 time-course and listener's MEG. Similar levels of right-hemisphere-predominant coherence were found at ˜0.5 Hz in Exps 1-3. Dynamic imaging of coherent sources revealed that the most coherent brain regions were located in the right posterior superior temporal sulcus (pSTS) and posterior superior temporal gyrus (pSTG) in Exps 1-2 and in the right supratemporal auditory cortex in Exp 3. Comparison between speech rhythm and phrasing suggested a connection of the observed coherence to pauses at the sentence level both in the spoken and hummed text. These results demonstrate significant coupling at ∼0.5 Hz between reader's voice and listener's cortical signals during listening to natural continuous voice. The observed coupling suggests that voice envelope fluctuations, due to prosodic rhythmicity at the phrasal and sentence levels, are reflected in the listener's cortex as rhythmicity of about 2-s cycles. The predominance of the coherence in the right pSTS and pSTG suggests hemispherical asymmetry in processing of speech sounds at subsentence time scales.

Low-frequency oscillations of the neural drive to the muscle are increased with experimental muscle pain

We investigated the influence of nociceptive stimulation on the accuracy of task execution and motor unit spike trains during low-force isometric contractions. Muscle pain was induced by infusion of hypertonic saline into the abductor digiti minimi muscle of 11 healthy men. Intramuscular EMG signals were recorded from the same muscle during four isometric contractions of 60-s duration at 10% of the maximal force [maximal voluntary contraction (MVC)] performed before injection (baseline), after injection of isotonic (control) or hypertonic saline (pain), and 15 min after pain was no longer reported. Each contraction was preceded by three 3-s ramp contractions from 0% to 10% MVC. The low-frequency oscillations of motor unit spike trains were analyzed by the first principal component of the low-pass filtered spike trains [first common component (FCC)], which represents the effective neural drive to the muscle. Pain decreased the accuracy of task performance [coefficient of variation (CoV) for force: baseline, 2.8 ± 1.8%, pain, 3.9 ± 1.8%; P < 0.05] and reduced motor unit discharge rates [11.6 ± 2.3 pulses per second (pps) vs. 10.7 ± 1.7 pps; P < 0.05]. Motor unit recruitment thresholds (2.2 ± 1.2% MVC vs. 2.4 ± 1.6% MVC), interspike interval variability (18.4 ± 4.9% vs. 19.1 ± 5.4%), strength of motor unit short-term synchronization [common input strength (CIS) 1.02 ± 0.44 vs. 0.83 ± 0.22], and strength of common drive (0.47 ± 0.08 vs. 0.47 ± 0.06) did not change across conditions. The FCC signal was correlated with force (R = 0.45 ± 0.06), and the CoV for FCC increased in the painful condition (5.69 ± 1.29% vs. 7.83 ± 2.61%; P < 0.05). These results indicate that nociceptive stimulation increased the low-frequency variability in synaptic input to motoneurons.

Decorrelation of cortical inputs and motoneuron output

Oscillations in the primary motor cortex are transmitted through the corticospinal tract to the motoneuron pool. This pathway is believed to produce an effective and direct command from the motor cortex to the spinal motoneurons for the modulation of the force output. In this study, we used a computational model of a population of motoneurons to investigate the factors that can influence the transmission of the cortical input to the output of motoneurons, since it can be quantified by coherence analysis. The simulations demonstrated that, despite the nonlinearity of the motoneurons, oscillations present in the cortical input are transmitted to the output of the motoneuron pool at the same frequency. However, the interference introduced by the nonlinearity of the system increases the variability of the oscillations in output, introducing spectral lines whose frequency depends on the input frequencies and the motoneuron discharge rates. Moreover, an additional source of synaptic input common to all motoneurons but independent from the corticospinal component decorrelates the cortical input and motoneuron output and, thus, decreases the magnitude of the estimated coherence, even if the effective cortical drive does not change. These results indicate that the corticospinal input can effectively be sampled by a small population of motoneurons. However, the transmission of a corticospinal drive to the motoneuron pool is influenced by the nonlinearity of the spiking processes of the active motoneurons and by synaptic inputs common to the motoneuron population but independent from the cortical input.

Functional community analysis of brain: a new approach for EEG-based investigation of the brain pathology

Analysis of structure of the brain functional connectivity (SBFC) is a fundamental issue for understanding of the brain cognition as well as the pathology of brain disorders. Analysis of communities among sub-parts of a system is increasingly used for social, ecological, and other networks. This paper presents a new methodology for investigation of the SBFC and understanding of the brain based on graph theory and community pattern analysis of functional connectivity graph of the brain obtained from encephalograms (EEGs). The methodology consists of three main parts: fuzzy synchronization likelihood (FSL), community partitioning, and decisions based on partitions. As an example application, the methodology is applied to analysis of brain of patients with attention deficit/hyperactivity disorder (ADHD) and the problem of discrimination of ADHD EEGs from healthy (non-ADHD) EEGs.

Fuzzy synchronization likelihood with application to attention-deficit/hyperactivity disorder

Synchronization as a measure of quantification of similarities in dynamic systems is an important concept in many scientific fields such as nonlinear science, neuroscience, cardiology, ecology, and economics. When interdependencies and connections of coupled dynamic systems are not directly accessible and measurable such as those of the neurons of the brain, quantification of similarities between their time series outputs is the best possible way to detect the existent interdependencies among them. In recent years, Synchronization Likelihood (SL) has been used as one of the most suitable algorithms in highly nonlinear and non-stationary systems. In this method, the likelihood of patterns is measured statistically, and then it is determined which patterns of the time series are similar to each other considering a threshold. But the degree of similarities is not considered in the decision. In this paper, a new measure of synchronization, fuzzy SL, is presented using the theory of fuzzy logic and Gaussian membership functions. The new fuzzy SL is compared with the conventional SL using both a standard problem from the chaos literature and a complicated real life neurological diagnostic problem, that is, the EEG-based diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD). The results of ANOVA analysis indicate the interdependencies measured by the fuzzy SL are more reliable than the conventional SL for discriminating ADHD patients from healthy individuals.

Fluctuations in isometric muscle force can be described by one linear projection of low-frequency components of motor unit discharge rates

The aim of the study was to investigate the relation between linear transformations of motor unit discharge rates and muscle force. Intramuscular (wire electrodes) and high-density surface EMG (13 x 5 electrode grid) were recorded from the abductor digiti minimi muscle of eight healthy men during 60 s contractions at 5%, 7.5% and 10% of the maximal force. Spike trains of a total of 222 motor units were identified from the EMG recordings with decomposition algorithms. Principal component analysis of the smoothed motor unit discharge rates indicated that one component (first common component, FCC) described 44.2 +/- 7.5% of the total variability of the smoothed discharge rates when computed over the entire contraction interval and 64.3 +/- 10.2% of the variability when computed over 5 s intervals. When the FCC was computed from four or more motor units per contraction, it correlated with the force produced by the muscle (62.7 +/- 10.1%) by a greater degree (P < 0.001) than the smoothed discharge rates of individual motor units (41.4 +/- 7.8%). The correlation between FCC and the force signal increased up to 71.8 +/- 13.1% when the duration and the shape of the smoothing window for discharge rates were similar to the average motor unit twitch force. Moreover, the coefficients of variation (CoV) for the force and for the FCC signal were correlated in all subjects (R(2) range = 0.14-0.56; P < 0.05) whereas the CoV for force was correlated to the interspike interval variability in only one subject (R(2) = 0.12; P < 0.05). Similar results were further obtained from measures on the tibialis anterior muscle of an additional eight subjects during contractions at forces up to 20% of the maximal force (e.g. FCC explained 59.8 +/- 11.0% of variability of the smoothed discharge rates). In conclusion, one signal captures most of the underlying variability of the low-frequency components of motor unit discharge rates and explains large part of the fluctuations in the motor output during isometric contractions.

Comparison between the degree of motor unit short-term synchronization and recurrence quantification analysis of the surface EMG in two human muscles

OBJECTIVE

To verify if non-linear recurrence analysis of the surface EMG is a suitable tool for assessing motor unit short-term synchronization.

METHODS

Surface and intramuscular EMG signals were recorded from the abductor digiti minimi and vastus medialis muscles of 12 and 10 healthy men, respectively, during isometric contractions. In the abductor digiti minimi, EMG signals were additionally recorded after a contraction sustained for 1min at 50% of the maximal force. In both muscles, percent of determinism (%DET) was estimated from the surface EMG and common input strength (CIS) index was computed from motor unit recordings.

RESULTS

For both muscles, CIS did not correlate with %DET (abductor digiti minimi: R(2)=0.11, P=0.12; vastus medialis: R(2)=0.04, P=0.56). Although the values of CIS for the vastus medialis were lower than those of the abductor digiti minimi (P<0.001), the %DET values did not differ between the two muscles (71.6+/-5.5% vs 66.9+/-8.7%; P=0.12).

CONCLUSION

The variable %DET extracted from the surface EMG is a poor indicator of the degree of motor unit short-term synchronization.

SIGNIFICANCE

The study provides a systematic evaluation of a technique previously proposed for the estimation of a clinically relevant characteristic of motor unit behavior.

Effect of cancellation on triggered averaging used to determine synchronization between motor unit discharge in separate muscles

Synchronization between single motor unit (SMU) discharges in separate muscles has been estimated from peaks in averaged electromyographic (EMG) recordings from one muscle triggered from SMU discharge in another. This study evaluated the effect of EMG signal cancellation on this measure of synchronization. SMU activity was recorded with 8 fine-wire electrodes in vastus medialis obliquus (VMO) and vastus lateralis (VL) during gentle isometric knee extension in 7 subjects. Data from 5 VL recordings were summed then rectified, or rectified then summed, to produce multi-unit recordings with and without cancellation, respectively. Averages of summed VL data were triggered from VMO SMUs. Synchronization, defined as a peak >3 SD above the triggered average mean, occurred in 73.68% and 78.95% of recordings with and without cancellation, respectively. To further investigate the effect of cancellation on synchronization, 250 "virtual" EMG recordings were created from VL data. VL SMUs were sorted and modified with respect to discharge rate, amplitude and polarity to create a collection of possible SMU discharge patterns. Virtual recordings were added one-by-one to VL recordings that showed synchronization. Virtual channels were rectified then added or added then rectified, to create data with and without cancellation. Identification of synchronization decreased similarly in both conditions with addition of virtual data. Our data show estimation of synchronization from triggered averages is more likely to detect synchronization in recordings with fewer SMUs, but cancellation has little effect. Synchronization must be interpreted with caution if number of SMUs changes between conditions.

Amplitude cancellation of motor-unit action potentials in the surface electromyogram can be estimated with spike-triggered averaging

The study presents analytical, simulation, and experimental analyses of amplitude cancellation of motor-unit action potentials (APs) in the interference electromyogram (EMG) and its relation to the size of the spike-triggered average (STA) EMG. The amount of cancellation of motor-unit APs decreases monotonically as a function of the ratio between the root mean square (RMS) of the motor-unit AP and the RMS of the interference EMG signal. The theoretical derivation of this association indicates a method to measure cancellation in individual motor units by STA of the interference and squared EMGs. The theoretical relation was examined in both simulated EMG signals generated by populations of 200 motor units and experimental recordings of 492 and 184 motor-unit APs in the vastus medialis and abductor digiti minimi muscles, respectively. Although the theoretical relation predicted (R2 = 0.95; P < 0.001) the amount of cancellation in the simulated EMGs, the presence of motor-unit synchronization decreased the strength of the association for small APs. The decrease in size of the STA obtained from the squared EMG relative to that extracted from the interference EMG was predicted by the experimental measure of cancellation (R2 = 0.65; P < 0.001, for vastus medialis; R2 = 0.26; P < 0.05, for abductor digiti minimi). The results indicate that cancellation of APs in the interference EMG can be analytically predicted and experimentally measured with STA from the discharge times of the motor units into the surface EMG.

Novel parameters of surface EMG in patients with Parkinson's disease and healthy young and old controls

The aim of this study was to evaluate a variety of traditional and novel surface electromyography (SEMG) characteristics of biceps brachii muscle in patients with Parkinson's disease (PD) and compare the results with the healthy old and young control subjects. Furthermore, the aim was to define the optimal biceps brachii loading level that would most likely differentiate patients from controls. The results indicated that such nonlinear SEMG parameters as %Recurrence, %Determinism and SEMG distribution kurtosis, correlation dimension and sample entropy were significantly different between the PD patients and healthy controls. These novel nonlinear parameters, unlike traditional spectral or amplitude parameters, correlated with the Unified Parkinson's Disease Rating Scale (UPDRS) and finger tapping scores. The most significant between group differences were found in the loading condition where no additional weights were applied in isometric elbow flexion. No major difference of SEMG characteristics was detected between old and young control subjects. In conclusion, the novel SEMG parameters can differentiate the patients with PD from healthy control subjects and these parameters may have potential in the assessment of the severity of PD.

A novel electromyographic signal simulator for muscle contraction studies

Mathematical simulation has been widely used in biomedical and biological sciences. In the case of the surface electromyographic (SEMG) activity, some models have been proposed aiming to study muscle contraction strategies that are used during different tasks and conditions. Most of SEMG simulators are based on energy modulation of a Gaussian noise. This work proposes a novel simulator in which the user-defined parameters are associated with the motor units (MUs) recruitment and their firing rate. Comparison between the mean spectrum of real SEMG signals collected in isometric contraction of the muscle biceps brachii and the mean spectrum obtained from simulated SEMG signals showed a good agreement, pointing the proposed simulator seems to be capable to generate consistent electromyographic signals in time and frequency domains and that can be used in many studies, in particular in the evaluation of automatic methods aimed to detect muscular contraction.

A simulation method for the firing sequences of motor units

The firing sequences of motoneurons contain important information with regard to the underlying neural processes. Several methods have been proposed in the literature to simulate these sequences, however, one of the limitations is that they are not capable of simulating the complex neural dynamics of motor neurons, especially those of concurrently active ones, such as motor unit synchrony and motor unit common drive. In this paper, a novel model based on the Hodgkin-Huxley (HH) system is proposed, which has the ability to simulate the complex neurodynamics of the firing sequences of motor neurons. The model is presented at the cellular level and network level, and some simulation results from a simple 3-neuron network are presented to demonstrate its applications.

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.

Motor unit synchronization measured by cross-correlation is not influenced by short-term strength training of a hand muscle

The purpose of the study was to quantify the strength of motor unit synchronization and coherence from pairs of concurrently active motor units before and after short-term (4-8 weeks) strength training of the left first dorsal interosseous (FDI) muscle. Five subjects (age 24.8 +/- 4.3 years) performed a training protocol three times/week that consisted of six sets of ten maximal isometric index finger abductions, whereas three subjects (age 27.3 +/- 6.7 years) acted as controls. Motor unit activity was recorded from pairs of intramuscular electrodes in the FDI muscle with two separate motor unit recording sessions obtained before and after strength training (trained group) or after 4 weeks of normal daily activities that did not involve training (control group). The training intervention resulted in a 54% (45.2 +/- 8.3 to 69.5 +/- 13.8 N, P = 0.001) increase in maximal index finger abduction force, whereas there was no change in strength in the control group. A total of 163 motor unit pairs (198 single motor units) were examined in both subject groups, with 52 motor unit pairs obtained from 10 recording sessions before training and 51 motor unit pairs from 10 recording sessions after training. Using the cross-correlation procedure, there was no change in the strength of motor unit synchronization following strength training (common input strength index; 0.71 +/- 0.41 to 0.67 +/- 0.43 pulses/s). Furthermore, motor unit coherence z scores at low (0-10 Hz; 3.9 +/- 0.3 before to 4.4 +/- 0.4 after) or high (10-30 Hz; 1.7 +/- 0.1 before to 1.9 +/- 0.1 after) frequencies were not influenced by strength training. These motor unit data indicate that increases in strength following several weeks of training a hand muscle are not accompanied by changes in motor unit synchronization or coherence, suggesting that these features of correlated motor unit activity are not important in the expression of muscle strength.

Modeling of muscle motor unit innervation process correlation and common drive

Concurrently active motor units (MUs) of a given muscle can exhibit a certain degree of synchronous firings, and a certain degree of common variation in their firing rates. The former property is referred to as motor unit synchrony in the literature, which is termed motor unit innervation process (MUIP) correlation in this study. The latter is referred to as motor unit common drive and can be quantified by the common drive coefficient, which is the correlation coefficient between the smoothed firing rates of the two MUs. Both properties have important roles and implications in the generation and resulting characteristics of the myoelectric signal and for the development of signal processing algorithms in myoelectric signal (MES) applications. In order to study these implications and characteristics, in this paper estimation procedures are developed to quantify the degree of MUIP correlation and common drive as functions of physiological parameters. Also, the interaction between MUIP correlation and motor unit common drive is studied in a physiologically realistic simulation model. Neurons modeled by Hodgkin-Huxley systems form the framework of the simulation model in which excitation and synaptic characteristics can be modified. MUIP correlation and common drive degree and interaction are studied through a number of simulations. To support the simulation results, experimental in vivo motor unit trains were collected at low levels of contraction from 11 subjects, and decomposed into the constituent unit trains giving 50 concurrently active motor unit pairs. The simulation demonstrated that the innervation process correlation coefficient is controlled primarily by the postsynaptic conductance, gsyn, and was less than 0.05 mS/cm2 for realistic values of gsyn. The common drive was found to be controlled by the exciting neuron input with no statistically significant interaction between it and the MUIP correlation. The experimental data gave results in close agreement with those of the simulation.

Motor unit synchronous firing as revealed by determinism of surface myoelectric signal

Information on motor strategies can be extracted from the surface electromyogram (EMG) by non-linear methods. The percentage of determinism (%DET) obtained from recurrence quantification analysis (RQA) may be a sensitive variable to detect synchronous motor unit behaviour. The purpose of the present study was to validate this methodology by comparing it with an established technique estimating the degree of synchronization of pairs of voluntary activated motor units from the correlation of their firing in the time-domain. Single motor unit activity was recorded in extensor carpi radialis (ECR) muscle by pairs of tungsten microelectrodes inserted into the muscle belly. Cross-correlation analysis was performed in order to determine synchronization peak area by computing synchronous impulse probability. Surface EMG activity was recorded in parallel by electrodes placed over the skin of the same muscle and %DET was used as a measure of synchronous activity. The %DET appeared to be a valid measure of synchronization yielding results comparable to those obtained with cross-correlation analysis. Increases in %DET (t = 64.59, P < 0.0001) highly correlated (r2 = 0.70, P = 0.0013) with pharmacologically induced increases in the synchronization activity of pairs of ECR motor units (t = 8.71, P < 0.0001). RQA may be used as an alternative methodology for testing synchronous motor unit behaviour from surface EMG under physiological and pathological conditions.

Amplitude cancellation reduces the size of motor unit potentials averaged from the surface EMG

The purpose of the study was to evaluate the influence of selected physiological parameters on amplitude cancellation in the simulated surface electromyogram (EMG) and the consequences for spike-triggered averages of motor unit potentials derived from the interference and rectified EMG signals. The surface EMG was simulated from prescribed recruitment and rate coding characteristics of a motor unit population. The potentials of the motor units were detected on the skin over a hand muscle with a bipolar electrode configuration. Averages derived from the EMG signal were generated using the discharge times for each of the 24 motor units with lowest recruitment thresholds from a population of 120 across three conditions: 1) excitation level; 2) motor unit conduction velocity; and 3) motor unit synchronization. The area of the surface-detected potential was compared with potentials averaged from the interference, rectified, and no-cancellation EMGs. The no-cancellation EMG comprised motor unit potentials that were rectified before they were summed, thereby preventing cancellation between the opposite phases of the potentials. The percent decrease in area of potentials extracted from the rectified EMG was linearly related to the amount of amplitude cancellation in the interference EMG signal, with the amount of cancellation influenced by variation in excitation level and motor unit conduction velocity. Motor unit synchronization increased potentials derived from both the rectified and interference EMG signals, although cancellation limited the increase in area for both potentials. These findings document the influence of amplitude cancellation on motor unit potentials averaged from the surface EMG and the consequences for using the procedure to characterize motor unit properties.

The contribution of motor unit pairs to the correlation functions computed from surface myoelectric signals

The contribution of motor unit action potential trains (MUAPT) of distinct motor units (MU) to the crosscorrelation function between myoelectric signals (MES) recorded at the skin surface is studied. In specific, the significance of the correlation between the firing activity of concurrently active MUs (which results in cross-terms in the overall correlation function) is compared to the representation obtained using the contributions of single MUs at each recording site (auto-terms). A model for the generation of surface MUAPs is combined with the generation of MU firing statistics in order to obtain surface MUAPTs. MU firing statistics are simulated to incorporate MU synchronization levels reported in the literature. Alternatively, experimental firing statistics are fed to the model generating the MUAPTs. The contribution of individual MU pairs to the global myoelectric signal correlation function is assessed. Results indicate that the cross-terms from different MUs decrease steadily contributing very little to the overall correlation for record lengths as short as 30 s. Thus, the error expected when computing the crosscorrelation function between two channels of MES as the superposition of the auto-terms contributed by single MUs (i.e., ignoring the cross-terms from different MUs) is shown to be very small.

A simulation study to examine the effect of common motoneuron inputs on correlated patterns of motor unit discharge

The influence of common oscillatory inputs to the motoneuron pool on correlated patterns of motor unit discharge was examined using model simulations. Motor unit synchronization, in-phase fluctuations in mean firing rates known as 'common drive', and the coefficient of variation of the muscle force were examined as the frequency and amplitude of common oscillatory inputs to the motoneuron pool were varied. The amount of synchronization, the peak correlation between mean firing rates and the coefficient of variation of the force varied with both the frequency and amplitude of the common input signal. Values for 'common drive' and the force coefficient of variation were highest for oscillatory inputs at frequencies less than 5 Hz, while synchronization reached a maximum when the frequency of the common input was close to the average motor unit firing rate. The frequency of the common input signal for which the highest levels of synchronization were observed increased as motoneuron firing rates increased in response to higher target force levels. The simulation results suggest that common low-frequency oscillations in motor unit firing rates and short-term synchronization result from oscillatory activity in different bands of the frequency spectrum of shared motoneuron inputs. The results also indicate that the amount of synchronization between motor unit discharges depends not only on the amplitude of the shared input signal, but also on its frequency in relation to the present firing rates of the individual motor units.

Motor unit synchronization between medial and lateral vasti muscles

OBJECTIVE

Accurate neuromuscular control of the patellofemoral joint is important in knee joint mechanics. Strategies to coordinate the vasti muscles, such as motor unit synchronization, may simplify control of patellar tracking. This study investigated motor unit synchronization between vastus medialis (VM) and lateralis (VL).

METHODS

Electromyographic (EMG) recordings of single motor unit action potentials (MUAPs) were made from VM and single- and multi-unit recordings were made from VL. Synchronization was quantified from peaks in the cross-correlogram generated from single MUAP pairs in VL and VM. The proportion of motor units in VM with synchronized firing in VL was also quantified from peaks in averages of multiunit VL EMG triggered from the VM MUAP.

RESULTS

A high degree of synchronization of motor unit firing between VM and VL was identified. Results were similar for cross-correlation ( approximately 45% of cases) and triggered averages (approximately 41% of cases).

CONCLUSIONS

The data suggest that synchronization between VM and VL is higher than expected. Agreement between traditional cross-correlation and triggered averaging methods suggest that this new technique may provide a more clinically viable method to quantify synchronization.

SIGNIFICANCE

High synchronization between VM and VL may provide a solution to simplify control of the mechanically unstable patellofemoral joint.

Motor unit synchronization of the vasti muscles in closed and open chain tasks

OBJECTIVES

To investigate motor unit synchronization between medial and lateral vasti and whether such synchronization differs in closed and open chain tasks.

DESIGN

Electromyographic recordings of single motor unit action potentials were made from the vastus medialis obliquus (VMO) and multiunit recordings from vastus lateralis during isometric contractions at 30 degrees of knee flexion in closed and open chain conditions.

SETTING

Laboratory.

PARTICIPANTS

Five volunteers with no history of knee pain (age, 30+/-3.32 y).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURE

The degree of synchronization between motor unit firing was evaluated by identifying peaks in the electromyographic averages of the vastus lateralis, triggered from motor unit action potentials in the VMO, and the proportion of power in the power spectral density of the triggered average at the firing frequency of the reference motor unit. The proportion of cases in which there was significant power and peaks in the triggered averages was calculated.

RESULTS

The proportion of trials with peaks in the triggered averages of the vastus lateralis electromyographic activity was greater than 61.5% in all tasks, and there was a significantly greater proportion of cases where power in the spectrum was greater than 7.5% ( P =.01) for the closed chain condition.

CONCLUSIONS

There was a high proportion of synchronized motor units between the 2 muscles during isometric contractions, with evidence for greater common drive between the VMO and vastus lateralis in closed chain tasks. This has implications for rehabilitation because it suggests that closed chain tasks may generate better coordination between the vasti muscles.

Motor-unit coherence and its relation with synchrony are influenced by training

The purpose of the study was to quantify the strength of motor-unit coherence from the left and right first dorsal interosseous muscles in untrained, skill-trained (musicians), and strength-trained (weightlifters) individuals who had long-term specialized use of their hand muscles. The strength of motor-unit coherence was quantified from a total of 394 motor-unit pairs in 13 subjects using data from a previous study in which differences were found in the strength of motor-unit synchronization depending on training status. In the present study, we found that the strength of motor-unit coherence was significantly greater in the left compared with the right hand of untrained right-handed subjects with the largest differences observed between 21 and 24 Hz. The strength of motor-unit coherence was lower in both hands of skill-trained subjects (21-27 Hz) and the right (skilled) hand of untrained subjects (21-24 Hz), whereas the largest motor-unit coherence was observed in both hands of strength-trained subjects (3-9 and 21-27 Hz). A strong curvilinear association was observed between motor-unit synchronization and the integral of coherence at 10-30 Hz in all motor-unit pairs (r2 = 0.77), and was most pronounced in strength-trained subjects (r2 = 0.90). Furthermore, this association was accentuated when using synchronization data with broad peaks (>11 ms), suggesting that the 10- to 30-Hz coherence is due to oscillatory activity in indirect branched common inputs. The altered coherence with training may be due to an interaction between cortical inhibition and the number of direct common inputs to motor neurons in skill- or strength-trained hands.

Time series analysis of jaw muscle contraction and tissue deformation during mastication in miniature pigs

Masticatory muscle contraction causes both jaw movement and tissue deformation during function. Natural chewing data from 25 adult miniature pigs were studied by means of time series analysis. The data set included simultaneous recordings of electromyography (EMG) from bilateral masseter (MA), zygomaticomandibularis (ZM) and lateral pterygoid muscles, bone surface strains from the left squamosal bone (SQ), condylar neck (CD) and mandibular corpus (MD), and linear deformation of the capsule of the jaw joint measured bilaterally using differential variable reluctance transducers. Pairwise comparisons were examined by calculating the cross-correlation functions. Jaw-adductor muscle activity of MA and ZM was found to be highly cross-correlated with CD and SQ strains and weakly with MD strain. No muscle's activity was strongly linked to capsular deformation of the jaw joint, nor were bone strains and capsular deformation tightly linked. Homologous muscle pairs showed the greatest synchronization of signals, but the signals themselves were not significantly more correlated than those of non-homologous muscle pairs. These results suggested that bone strains and capsular deformation are driven by different mechanical regimes. Muscle contraction and ensuing reaction forces are probably responsible for bone strains, whereas capsular deformation is more likely a product of movement.

Properties of human motor units after prolonged activity at a constant firing rate

The primary purpose of this study was to examine if there are changes in the intrinsic properties of spinal motoneurons after prolonged submaximal contractions. To do this, we assessed whether or not the synaptic drive to motoneurons needs to increase in order to maintain a constant firing rate of a motor unit. Recruitment of new units and an increase in total electromyographic (EMG) activity of the muscle of interest were taken as estimates of an increase in synaptic drive. Subjects were asked to maintain a constant firing rate of a clearly identifiable (targeted) motor unit from the first dorsal interosseous muscle for approximately 10 min, while surface EMG and force were recorded simultaneously. For the 60 units studied, the duration of the constant-firing-rate period ranged from 73 to 1,140 s (448 +/- 227 s; mean +/- SD). There was a significant increase ( t-test, p<0.001) in the magnitude of mean surface EMG, and DC force while the targeted motoneuron maintained a constant rate suggesting an increase in the net excitatory input to the motoneuron pool. Changes occurring simultaneously in other parameters, namely, variability in interspike interval, magnitude of force fluctuations, the duration of motor unit action potentials, and the median power frequency of surface EMG were also computed. The firing rates of 16 concurrently firing motoneurons, not controlled by the subject, remained constant. The key finding of this study is that after prolonged activity, a motoneuron requires a stronger excitatory input to maintain its firing rate. Additional results are indicative of significant changes in the characteristics of the synaptic inputs, changes at the neuromuscular junction (both pre- and postsynaptic regions) and the sarcolemma.

Crosstalk in surface electromyography of the proximal forearm during gripping tasks

Electromyographic (EMG) crosstalk was systematically analyzed to evaluate the magnitude of common signal present between electrode pairs around the forearm. Surface EMG was recorded and analyzed from seven electrode pairs placed circumferentially around the proximal forearm in six healthy individuals. The cross-correlation function was used to determine the amount of common signal, which was found to decrease as the distance between electrode pairs increased, but was not significantly altered by forearm posture (pronation, neutral, supination). Overall, approximately 40% common signal was detected between adjacent electrode pairs (3 cm apart), dropping to about 10% at 6 cm spacing and 2.5% at 9 cm. The magnitude of common signal approached 50% between adjacent electrode pairs over the extensor muscles, while over 60% was observed between neighbouring sites on the flexor aspect of the forearm. Although flexor and extensor EMG amplitude was similar, less than 2% common signal was present between flexor and extensor electrode pairs during both pinch and grasp tasks. Maximum grip force production was not affected by forearm rotation for pinch, but reduced 18% from neutral (mid-prone) to pronation during grasp (p=0.01). In spite of differences in grip force, mean muscle activity did not vary between the three forearm postures during maximum pinch or grasp trials. While this study improved our knowledge of crosstalk and electrode spacing issues, further examination of forearm EMG is required to improve understanding of muscle loading, EMG properties and motor control during gripping tasks.

Deconvolution estimation of motor unit conduction velocity distribution

A conduction velocity distribution (CVD) estimator that incorporates volume conductor modeling of the muscle voluntary response is introduced in this paper. The CVD estimates are obtained from two correlation functions, an autocorrelation and a cross, computed from myoelectric signal recorded at the skin surface. The performance of the proposed estimator is evaluated for simulated and experimental data. The study includes assessment of the estimator bias and standard deviation, as well as its sensitivity to errors in the model parameters. Simulations show its good performance in terms of estimator bias. A filtering technique also helps reduce its variance. However, the inaccuracy introduced in the estimation of model parameters considerably deteriorates the estimator performance.