Effect of EMG Biofeedback and Progressive Muscle Relaxation Training on Awareness of Frontalis Muscle Tension

Awareness of muscle tension, as estimated by a modification of the Kinsman et al. (1975) procedure for determining probability of correct estimation (P(c)) of absolute differences in muscle tension between adjacent trials, was examined before and after volunteer subjects underwent 4 sessions of either: 1) EMG biofeedback (BF) training, 2) progressive muscle relaxation (PMR) training, or 3) a placebo-control (MC) procedure which involved listening to music as an alleged guide for relaxation. The subjects were 30 females (mean age = 28.3 yrs) responding to an offering of experimental treatment for anxiety and tension. Measurements of frontalis muscle tension (EMG) and P(c) were made before and after training. The results showed that EMG was significantly reduced by BF and PMR training but not by the MC procedure. Increases in P(c) after training were significantly greater for BF than for PMR or MC training. There were no group differences for subjective report of tension. Correlations between pre- to post-training EMG and P(c) change scores were significant only for the BF group and the combined group of BF and PMR subjects. These results suggest that: 1) both BF and PMR training were effective in producing frontalis EMG reductions, 2) the following relationship may exist among training groups in terms of relative influence upon awareness of tension—BF training > PMR training > MC training, and 3) awareness of tension appears to be related to the ability to reduce EMG although the exact nature of this relationship remains unclear.     

Signal characteristics of EMG during fatigue

Summary Electromyographic (EMG) activity of m. rectus femoris muscle was registered from young male and female subjects during maintained isometric knee extension at 60% of maximal voluntary contraction. The following EMG parameters were analyzed for the entire fatigue time: integrated EMG (IEMG), averaged motor unit potential (AMUP) and power spectral density function (PSDF). The results indicated a slight but continuous rise of IEMG during the fatigue period. AMUP showed sensitivity to fatigue with increase in amplitude, rise time, and number of spikes counted. PSDF was also easily affected by fatigue so that the total power density curve was shifted towards lower frequencies with a high frequency decay. The mean power frequency decreased linearily as a function of fatigue time. The findings suggest that in addition to natural recruitment of new motor units the fatigue is characterized by marked reduction in the conduction velocities of action potential along the used muscle fibers.     

基于非线性能量算子的表面肌电信号MUAP发放检测

采用基于平滑非线性能量算子(smoothed nonlinear energy operator, SNEO)的方法对表面肌电(surface EMG, SEMG)信号运动单位动作电位(motor unit action potential, MUAP)的发放信息进行检测,提出一种能较精确确定MUAP发放数目的阈值检测方法.利用这些方法分别对肌肉轻度收缩和中度收缩时的SEMG信号进行了MUAP发放检测实验,结果表明,对于轻度收缩时的SEMG信号,本文的方法十分有效,而对中度收缩时的SEMG信号也能获得比较满意的检测结果.     

The Effect of Repeated Tense-Release Sequences on EMG and Self-Report of Muscle Tension: An Evaluation of Jacobsonian and Post-Jacobsonian Assumptions About Progressive Relaxation

This study tested three psychophysiological hypotheses generated from assumptions underlying post-Jacobsonian progressive relaxation techniques and assumptions that conflict with the rationale for Jacobson's progressive relaxation method. Twenty-eight subjects tensed and released tension from the forearm extensor and frontalis muscles during a series of trials interspersed with intervals of relaxation. Correlations between self-report of tension and EMG were moderate during relaxation, but did not increase across successive tense-release trials, thus indicating no increase in self-awareness of muscle tension. As predicted by Jacobson, paying attention to either muscle produced increased tension in the frontal area, but Jacobson's prediction of increases in forearm tension while paying attention to the forearm was not confirmed. Evidence was equivocal for the existence of the “pendulum effect” predicted by Bernstein and Borkovec. Tense-release trials produced gradual decreases in frontal EMG, but no changes in forearm EMG. Declines below baseline in self-reported muscle tension occurred only several minutes after the end of the tense-release trials, and did so for the forearm despite lack of EMG decreases in that area. This finding may reflect the influence of cognitive rather than muscular processes.     

The Change of Spectral Energy Distribution of Surface EMG Signal During Forearm Action Process

Spectral energy distribution of surface EMG signal is often used but difficultly and effectively control artificial limb, because the spectral energy distribution changes in the process of limb actions. In this paper, the general characteristics of surface EMG signal patterns were firstly characterized by spectral energy change. 13 healthy subjects were instructed to execute forearm supination （FS） and forearm pronation （FP） with their right foreanns when their forearm muscles were ＂fatigue＂ or ＂relaxed＂. All surface EMG signals were recorded from their right forearm flexor during their right forearm actions. Two sets of surface EMG signals were segmented from every surface EMG signal appropriately at preparing stage and acting stage. Relative wavelet packet energy （symbolized by pnp and pna respectively at preparing stage and acting stage, n denotes the nth frequency band） of surface EMG signal firstly was calculated and then, the difference （Pn = Pna-Pnp） were gained. The results showed that Pn from some frequency bands can effectively characterize the general characteristics of surface EMG signal patterns. Compared with Pn in other frequency bands, P4, the spectral energy change from 93.75 to 125 Hz, was more appropriately regarded as the features.     

Signal Characteristics of EMG with Special Reference to Reproducibility of Measurements

Reliability and constancy of recordings of EMG signal characteristics were investigated from the measurements taken with minitature size surface electrodes during submaximal and maximal contraction of the rectus femoris muscle. The following EMG variables were studied: integrated EMG (IEMG) various band-widths of the power spectral density function, mean power frequency (MPF), and rise time, amplitude and number of spikes of the averaged motor unit potential (AMUP). The results indicated that for most of the variables studied the reproducibility of measurements was better within the test session (reliability) than between the different test days (constancy). The reliability values for IEMG, MPF and AMUP amplitude were rather high (r = 0.77–0.92). MPF and number of spikes in AMUP showed good constancy values (r=0.73–0.93) and it is suggested that these parameters can be recommended for use in EMG studies where recordings are repeated over a period of several days.     

Lack of Correlation Between Frontalis EMG and Either Neck EMG or Verbal Ratings of Tension

Eight normal subjects were trained with the aid of EMG feedback to successively increase and decrease the activity of the frontalis muscle on 5 consecutive days. Along with the activity of the frontalis, sternomastoid EMG was recorded from 4 subjects and EMG from both the semispinalis and splenius capitus was recorded from the other 4 subjects. Estimates of the degree of subjective tension or relaxation were obtained following Baseline, Increase Frontalis, and Decrease Frontalis periods on each treatment day. Frontalis EMG activity showed significant increases and decreases relative to baseline levels during appropriate periods. Sternomastoid EMG did not change significantly during either Increase Frontalis or Decrease Frontalis periods. Semispinalis/splenius EMG activity also did not change during Increase Frontalis periods, but increased significantly during Decrease Frontalis periods. Subjects’estimates of subjective tension increased above baseline during increases in frontalis EMG activity, but did not change significantly during decreases in frontalis EMG. These data support the findings of Alexander that changes in frontalis EMG neither generalize to other somatic muscles nor correlate with verbal reports of cognitive tension or relaxation.     

Generalization of Conditioned Muscle Tension: A Closer Look

In EMG biofeedback it has been suggested that covariation in tension levels between a trained and untrained muscle increases with continued training of the target muscle. Predictions of this sort are in contrast to the notion of response generalization which would predict decreased covariation between trained and untrained muscles with continued training. This study examined covariation between facial and forearm EMG levels during facial EMG biofeedback, and evaluated changes in such covariation which occurred with continued facial EMG training. Two groups of individuals were trained either to increase or to decrease facial muscle tension in 3 laboratory sessions during which simultaneous measures of facial and forearm EMG were obtained at 1-min intervals. Comparison of facial EMG measures for these groups with those for a third, noncontingent control group revealed bidirectional conditioned effects for each training session. Some covariation between forearm and facial EMG was apparent for all groups. However, examination of change in covariation across sessions revealed that covariation decreased for groups receiving facial muscle training, whereas covariation levels exhibited by the noncontingent feedback group remained stable. This finding was consistent with the concept of response generalization but not with the notion of muscle tension generalization as employed in EMG biofeedback.     

用DFSA进行肌电信号的数据分析处理

本文叙述了采用ＦＳＡ原理，精确地实现复杂肌电信号的数据分析与处理。着重描述了用ＤＦＳＡ的方法产生肌电信号中单相波、双相波、多相波、转折等的状态转换图及转换原理，以及由此状态图实现的程序流程图。     

基于HHT变换的SEMG信号中MUAP的数目估计

对表面肌电（SEMG）信号中单位动作电位（MUAP）的数目进行估计可为神经肌肉控制的理论研究和神经肌肉疾病的诊断开辟新途径,本文给出了一种基于Hilbert-Huang变换（HHT）的表面肌电信号中运动MUAP数目估计方法.通过对SEMG信号经验模态分解后的第一内禀模态函数分量进行瞬时频率分析,利用其瞬时频率极值点的计数即可估计出运动MUAP数目.仿真信号与真实信号的实验结果均表明,基于HHT的SEMG信号中MUAP的估计方法是有效的.     

Theoretical analysis of surface EMG in voluntary isometric contraction

Summary A new stochastic model of the surface EMG is suggested and the spectral density of the surface EMG is studied theoretically and experimentally to confirm the validity of this model.Theoretical results show that while the contraction level is not so high, the shape of the spectral density (distribution) does not change and its amplitude is directly proportional to the motor unit firing frequency and recruitment.To illustrate the theoretical results, experiments were carried out for rectus femoris and biceps brachii. The surface EMG was lead off by bipolar surface electrodes. And the spectral density of the surface EMG was calculated using FFT algorithm.From these experimental results, it was confirmed that our theoretical results were almost valid.     

Experimental and modelling investigation of surface EMG spike analysis

A pattern classification method based on five measures extracted from the surface electromyographic (sEMG) signal is used to provide a unique characterization of the interference pattern for different motor unit behaviours. This study investigated the sensitivity of the five sEMG measures during the force gradation process. Tissue and electrode filtering effects were further evaluated using a sEMG model. Subjects (N=8) performed isometric elbow flexion contractions from 0 to 100% MVC. The sEMG signals from the biceps brachii were recorded simultaneously with force. The basic building block of the sEMG model was the detection of single fibre action potentials (SFAPs) through a homogeneous, equivalent isotropic, infinite volume conduction medium. The SFAPs were summed to generate single motor unit action potentials. The physiologic properties from a well-known muscle model and motor unit recruitment and firing rate schemes were combined to generate synthetic sEMG signals. The following pattern classification measures were calculated: mean spike amplitude, mean spike frequency, mean spike slope, mean spike duration, and the mean number of peaks per spike. Root-mean-square amplitude and mean power frequency were also calculated. Taken together, the experimental data and modelling analysis showed that below 50% MVC, the pattern classification measures were more sensitive to changes in force than traditional time and frequency measures. However, there are additional limitations associated with electrode distance from the source that must be explored further. Future experimental work should ensure that the inter-electrode distance is no greater than 1cm to mitigate the effects of tissue filtering.     

Adaptive certainty-based classification for decomposition of EMG signals

An adaptive certainty-based supervised classification approach for electromyographic (EMG) signal decomposition is presented and evaluated. Similarity criterion used for grouping motor unit potentials (MUPs) is based on a combination of MUP shapes and two modes of use of motor unit (MU) firing pattern information: passive and active. Performance of the developed classifier was evaluated using synthetic signals of known properties and real signals and compared with the performance of the certainty classifier (CC). Across the sets of simulated and real EMG signals used for comparison, the adaptive certainty classifier (ACC) had both better average performance and lower performance variability. For simulated signals of varying intensity, the ACC had an average correct classification rate (CC _r ) of 83.7% with a mean absolute deviation (MAD) of 5.8% compared to 78.3 and 8.7%, respectively, for the CC. For simulated signals with varying amounts of shape and/or firing pattern variability, the ACC had a CC _r of 79.7% with a MAD of 4.7% compared to 76.6 and 6.9%, respectively, for the CC. For real signals, the ACC had a CC _r of 70.0% with a MAD of 6.3% compared to 64.9 and 6.4%, respectively, for the CC. The test results demonstrate that the ACC can manage both MUP shape variability as well as MU firing pattern variability. The ACC adapts to EMG signal characteristics to create dynamic data driven classification criteria so that the number of MUP assignments made reflects the signal complexity and the number of erroneous assignments is kept sufficiently low. The ability of the ACC to adjust to specific signal characteristics suggests that it can be successfully applied to a wide variety of EMG signals.     

基于小波系数熵的表面肌电信号识别

目的:对表面肌电信号进行分类识别。方法:30名健康的志愿者参加数据采集。每名志愿者用右手臂完成两个动作:前臂内旋和前臂外旋。在每个动作中,采集一组表面EMG信号。总共获得30组内旋和30组外旋的表面EMG信号。然后,运用小波包系数熵构成特征向量,用Bayes决策对两种模式信号进行分类识别。结果:当信号长度达350ms后,正确识别率达到100%。结论:采用小波包系数熵可以有效地提取表面EMG信号的特征信息,达到控制前臂假肢的目的。     

Transfer of EMG Training: Another Look at the General Relaxation Issue

A study was made of the extent to which electromyographic (EMG) response-decrease training with biofeedback on each of two successive sites would affect the other site in terms of generalization and transfer of training. Thirty-two subjects were assigned to four groups of equal size. Groups Frontal-Arm and Arm-Frontal were first provided with auditory feedback for EMG responses on either the forehead (frontal) or forearm extensor areas in an initial set of training sessions and on the alternate area in a second set of training sessions. Control groups Relax-Arm and Relax-Frontal were provided with instructions designed to induce “motivated relaxation” on one of the two sites in initial training followed by biofeedback training on the alternate site in subsequent training. The EMG training, but not the instructions, resulted in reduced EMG potentials in both the forearm and frontal areas. However, training on one site did not appear to facilitate more rapid acquisition of reduced potentials (transfer of training) during subsequent biofeedback on the alternate site. There was also no evidence for generalized training effects in the form of reduced potentials on other sites, skin temperatures, or self-reports at the time of training on one site.     

A simulation model of the surface EMG signal for analysis of muscle activity during the gait cycle

This work describes a model able to synthetize the surface EMG (electromyography) signal acquired from tibialis anterior and gastrocnemious medialis muscles during walking of asymptomatic adult subjects. The model assumes a muscle structure where the volume conductor is represented by multiple layers of anisotropic media. This model originates from analysis of the single fiber action potential characterized by the conduction velocity. The surface EMG of voluntary contraction is calculated by gathering motor unit action potentials estimated by the summation of all activities of muscle fibers assumed to have a uniformly parallel distribution. The parameters related to the gait cycle, such as onset and cessation timings of muscle activation, amplitude of muscle contraction, periods and sequences of motor units' recruitment, are included in the model presented. In addition, the relative positions of the electrodes during gait can also be specified in order to adapt the simulation to the different acquisition settings.     

Robust supervised classification of motor unit action potentials

A certainty-based classification algorithm is described, which comprises part of a clinically used EMG signal decomposition system. This algorithm classifies a candidate motor unit action potential (MUAP) to the motor unit action potential train (MUAPT) that produces the greatest estimated certainty, provided this maximal certainty is above a given threshold. The algorithm is iterative, such that the certainty with which assignments are made increases with each pass through the data, and it has specific stopping criteria. The performance and sensitivity (to the assignment threshold) of the Certainty algorithm and an iterative minimum Euclidean distance (MED) algorithm are compared by classifying sets of MUAPs detected in real concentric needle-detected EMG signals, using a range of assignment thresholds for each algorithm. With regard to MUAP assignment and error rates, the Certainty algorithm consistently provides better mean results and, more importantly, less variable results than the MED algorithm. The Certainty algorithm can provide mean assignment and error rates of 80.8 and 1.5%, respectively, with a maximum error rate of 3.2%; the MED algorithm can provide mean assignment and error rates of 80.3 and 3.3%, respectively, with a maximum error rate of 6.5%. The Certainty algorithm is relatively insensitive to the certainty threshold used, can consistently differentiate between similarly shaped MUAPs from different MUAPTs, and can make correct classifications despite biological shape variability, background noise and signal shape non-stationarity.     

声图像法的骨骼肌多参数测量

通过自行开发研制的超声运动与测量系统,连续、同步获取了肱二头肌等长收缩活动时的B超图像力矩、肌电信号,并将超声扫描骨骼肌得到的有关骨骼肌结构的空间形态变化信息定义为“声肌图（sonomyography,SMG）。通过采集到的信号研究并讨论了肌肉厚度变化率SMG和表面肌电信号、力矩之间的关系,并给出了经验公式,它对了解骨骼肌功能、对骨骼肌建模以及对康复设备的研发等有重要的作用和意义。     

Effect of position of electrodes relative to the innervation zone onsurface EMG

We investigated the effect of the position of electrodes relative to the innervation zone (IZ) of the biceps brachii muscle during isometric elbow flexion using eight-channel surface array electrodes. We estimated the location of the IZ near the centre of the muscle in 20 male subjects. The pulse peaks from electromyogram (EMG) waveforms were detected for each channel and averaged, the triphasic pulse was determined, and the peak values of the first and third phases were compared. The results showed significantly greater pulse values for the first phase when the electrode placement was proximal to the estimated IZ, and for the third phase when the electrode placement was distal to the estimated IZ. Using this method, the positional relationship between electrodes and IZ can be determined using a surface EMG waveform recorded with a pair of bipolar electrodes. This method may be clinically useful in confirming the reliability of a recorded surface EMG.     

Contralateral influence on recruitment of curarized muscle fibres during maximal voluntary extension of the legs

The force of maximal voluntary contraction (MVC) was compared during extension of one leg alone and during simultaneous extension of both legs. In 6 subjects MVC of two leg extension was 75 + 3.6 (S.E.) % of the sum of the single one leg MVC. This may indicate a reduced muscle fibre involvement during two leg extension as compared with one leg extension. We investigated the possibility that either red (type I) or white (type II) muscle fibre recruitment was restricted during two leg extension. The neuromuscular transmission in the two types of fibres was partially blocked with d-tubocurarine (dtc) or decamethonium (C₁₀) for either type 1 or II, respectively. In 5 subjects receiving dtc the ratio between the reduced two leg and one leg extension forces (75 ± 2.3%) did not change. During administration of C₁₀, however, this ratio decreased by 16 ± 3.1 %. Partial blocking with dtc caused a two component curve during tension development with a first maximum at 0.22 s, and another maximum at 1.03 s of a 3 s attempt to reach MVC. During partial blocking with C₁₀ the first maximum disappeared indicating human fast twitch fibres to be blocked by C₁₀. Thus, partial blocking with C₁₀ results in muscle contractions where a relatively large amount of type I muscle fibres are contributing to the force developed. The experiment suggests that type I fibre recruitment is restricted during severe static exercise in normal muscles. Furthermore, it demonstrates that fibre recruitment can vary in the same muscle function performed with one leg or two legs.