A non-invasive method for measuring inspiratory muscle fatigue during progressive isocapnic hyperventilation in man

Eleven normal adults each performed a ten minute progressive isocapnic hyperventilation (PIHV) test in which ventilatory levels were increased every two minutes. All subjects exhibited mechanical fatigue by failing to maintain the target of 80% of maximum voluntary ventilation (MVV). The mean ventilation at this level was 67.5 +/- 1.4% MVV. This fatigue was accompanied by a fall in transdiaphragmatic pressure. During the test the EMG of the sternomastoid (SM) was monitored by surface electrodes and was analyzed using fast-fourier transform. The centroid frequency (Fc) fell as ventilation increased, and correlated negatively with the inability to achieve target ventilation(r = -0.99, p less than 0.015). Five subjects performed the test while the diaphragmatic EMG was recorded from an oesophageal electrode (DIes) and from surface electrodes (DIs). The Fc of DIes fell with increasing ventilation levels (r = -0.95, p less than 0.05) and there was a correlation between the Fc changes of both DIes and the SM (r = -0.92, p less than 0.001). The Fc of DIs did not correlate with either mechanical performance or the Fc of DIes, because of contamination of surface signals by signals from expiratory muscles. It is concluded that the PIHV along with surface monitoring of EMG activity from the sternomastoid can serve as a non-invasive method for evaluating inspiratory muscle fatigue.     

The effects of EMG-assisted relaxation training on the academic performance, locus of control, and self-esteem of hyperactive boys

This study investigated effects of EMG-assisted relaxation training on the academic performance, locus of control, and self-esteem of hyperactive junior high school-age boys. Twenty-four subjects each were randomly assigned to the treatment and control condition. Treatment consisted of six 20 to 25-minute sessions conducted biweekly. Pretreatment frontalis EMG, math, reading, and language performance, locus of control, and self-esteem were assessed for both groups. Outcome measures were again obtained on these dependent variables 2 weeks after the last treatment session. Experimental subjects demonstrated significantly higher posttreatment reading and language performance. Math performance gain did not reach statistical significance. A significant internal shift in locus of control was observed; however, self-esteem did not improve to that level. These outcomes correlated with significantly lower posttreatment frontalis EMG in the experimental group. EMG level did not change during the course of this study for control subjects. Implications of these findings are discussed in terms of existent research.     

Electromyographic biofeedback for tension control during fine motor skill acquisition

The presence of residual muscular tension has been implicated as a detrimental influence on the performance and learning of motor skills. A method for reducing muscular tension has been provided by the advent of biofeedback training. This study investigated the effects of tension-control training by electromyographic (EMG) biofeedback on learning and performance of the pursuit-rotor backing task. Thirty young adult males were pretested for pursuit-rotor (PR) tracking skill, ranked by performance scores, and divided into identical triplicates to form two experimental groups and a control group. After a total of 3 hours of EMG biofeedback training for the experimental groups, all subjects were reevaluated on the PR test. One experimental group received biofeedback during the posttests. Analysis of variance of pretest-posttest difference means andt tests of scores representing performance and tension indicated that the EMG biofeedback training (1) significantly reduced tension induced by the novel motor skill and (2) significantly improved performance of the motor skill. Transfer of tension-control training was shown to facilitate learning and performance more than direct EMG biofeedback during performance. Residual tension reduction during learning was particularly facilitated by EMG biofeedback training, a profound implication for the management of stress in a variety of situations.This investigation formed part of a Ph.D. dissertation research (1976) conducted by the author under the guidance of Dr. Donald E. Campbell, Department of Physical Education, and Dr. Carol A. Saslow, Department of Psychology, at Oregon State University.     

基于表面肌电信号的人体动作识别算法研究进展

表面肌电信号(Surface Electromyography,sEMG)是通过相应肌群表面的传感器记录下来的一维时间序列非平稳生物电信号,不但反映了神经肌肉系统活动,对于反映相应动作肢体活动信息同样重要。而模式识别是肌电应用领域的基础和关键。为了在应用基于表面肌电信号模式识别中选取合适算法,本文拟对基于表面肌电信号的人体动作识别算法进行回顾分析,主要包括模糊模式识别算法、线性判别分析算法、人工神经网络算法和支持向量机算法。模糊模式识别能自适应提取模糊规则,对初始化规则不敏感,适合处理s EMG这样具有严格不重复的生物电信号;线性判别分析对数据进行降维,计算简单,但不适合大数据;人工神经网络可以同时描述训练样本输入输出的线性关系和非线性映射关系,可以解决复杂的分类问题,学习能力强;支持向量机处理小样本、非线性的高维数据优势明显,计算速度快。比较各方法的优缺点,为今后处理此类问题模式识别算法选取提供了参考和依据。     

Use of supervised discretization with PCA in wavelet packet transformation-based surface electromyogram classification

This paper describes a preprocessing stage for nonlinear classifier used in wavelet packet transformation (WPT)-based multichannel surface electromyogram (EMG) classification. The preprocessing stage named sdPCA, which consists of supervised discretization coupled with principal component analysis (PCA), was developed for improving surface EMG classifier generalization ability and training speed on overlap segmented signals. The sdPCA outperforms the fast correlation-based filter (FCBF), PCA, supervised discretization, and their combinations in terms of the highest generalization ability, fast training speed, the small feature size, and an ability to reduce the risks of developing oscillation and being trapped in nonlinear classifier training. The experiments were conducted on a data set consisting of 4-channel surface EMG signals measured from 6 hand and wrist gestures of 12 subjects. The experimental results indicate that the classification system using sdPCA has the highest generalization ability along with the second fastest training speed. The classification accuracy in 12 subjects of the system using sdPCA is 93.30 ± 2.42% taking 400 epochs for training by overlap segmented signals within 100 s. This result is very attractive for further development because we can achieve high-classification accuracy for large data sets by means of the proposed sdPCA without the application of additional algorithms such as local discriminant bases (LDB), majority voting (MV), or WPT sub-bands clustering.     

Effectiveness of multiple-site EMG biofeedback in the reduction of arousal

During the training phase, 36 subjects received (a) EMG biofeedback from multiple muscle sites, (b) EMG biofeedback from the frontal site, or (c) no biofeedback. Results indicated that neither biofeedback procedure reduced self-reports of anxiety, but that multiple-site biofeedback was effective in reducing several indices of autonomic arousal (pulse rate, finger pulse volume, and skin temperature) while frontal biofeedback was not. During the generalization/stress phase, all subjects were threatened with and received electric shocks and were told to apply the relaxation techniques they learned during the training phase even though no additional biofeedback would be provided. Results indicated that multiple-site biofeedback was effective in reducing self-reports of anxiety and autonomic arousal but that frontal biofeedback was not. These results confirm previous data indicating that frontal biofeedback is not an effective procedure for controlling stress, but suggest that EMG biofeedback can be effective in reducing self-reported anxiety and autonomic arousal if a multiple muscle-site feedback procedure is employed.     

A non-MVC EMG normalization technique for the trunk musculature: Part 2. Validation and use to predict spinal loads.

Estimates of the amount of force exerted by a muscle using electromyography (EMG) rely partially upon the accuracy of the reference point used in the normalization technique. Accurate representations of muscle activities are essential for use in EMG-driven spinal loading models. The expected maximum contraction (EMC) normalization method was evaluated to explore whether it could be used to assess individuals who are not capable of performing a maximum exertion such as a person with a low back injury. Hence, this study evaluated the utility of an EMG normalization method (Marras and Davis, A non-MVC EMG normalization technique, Part 1, method development. Journal of Electromyography and Kinesiology 2000) that draws upon sub-maximal exertions to determine the reference points needed for normalization of the muscle activities. The EMC normalization technique was compared to traditional MVC-based EMG normalization by evaluating the spinal loads for 20 subjects (10 males and 10 females) performing dynamic lifts. The spinal loads (estimated via an EMG-assisted model) for the two normalization techniques were very similar with differences being <8%. The model performance variables indicated that both normalization techniques performed well (r(2)>0.9 and average error below 6%) with only the muscle gain being affected by normalization method as a result in different reference points. Based on these results, the proposed normalization technique was considered to be a viable method for EMG normalization and for use in EMG-assisted models. This technique should permit the quantitative evaluation of muscle activity for subjects unable to produce maximum exertions.     

Discrimination of Parkinsonian tremor from essential tremor by implementation of a wavelet-based soft-decision technique on EMG and accelerometer signals

A wavelet-decomposition with soft-decision algorithm is used to estimate an approximate power-spectral density (PSD) of both accelerometer and surface EMG signals for the purpose of discrimination of Parkinson tremor from essential tremor. A soft-decision wavelet-based PSD estimation is used with 256 bands for a signal sampled at 800 Hz. The sum of the entropy of the PSD in band 6 (7.8125–9.375 Hz) and band 11 (15.625–17.1875 Hz) is used as a classification factor. The data has been recorded for diagnostic purposes in the Department of Neurology of the University of Kiel, Germany. Two sets of data are used. The training set, which consists of 21 essential-tremor (ET) subjects and 19 Parkinson-disease (PD) subjects, is used to obtain the threshold value of the classification factor differentiating between the two subjects. The test data set, which consists of 20 ET and 20 PD subjects, is used to test the technique and evaluate its performance. A “voting” between three results obtained from accelerometer signal and two EMG signals is applied to obtain the final discrimination. A total accuracy of discrimination of 85% is obtained.     

Simultaneous biofeedback of heart rate and frontal EMG as a pretraining for the control of EEG theta activity

Following one base-line session, 20 normal subjects received four half hour sessions consisting of simultaneous feedback of heart rate and frontalis muscle (pretraining). Ten subjects received contingent (CF), the other ten noncontingent feedback (NCF). Subjects were asked to lower heart rate and frontal muscle tension (EMG). Heart rate within sessions decreased up to 19 bpm, with a mean of 4 bpm for the CF group. There was only a weak decrease over sessions, however, because of the strong habituation effect. The following events accompanied the heart rate decrease: (1) an increase of the variability of the heart rate, (2) a decrease of the variance of the EMG, (3) an increased correlation between heart rate slowing and EMG decrease, and (4) an increasing subjective experience of control of heart rate and EMG. After pretraining, subjects received eight sessions of auditory feedback of their frontal EEG theta activity (four sessions with CF and four sessions with NCF in balanced order). There was a weak increase of theta for the CF condition over sessions, but a decrease within the sessions. Pretraining on heart rate and frontal EMG control had no influence on the performance during theta training. It was hypothesized that control of heart rate slowing and theta control involve different mechanisms.     

Cardiovascular adjustments to rhythmic handgrip exercise: relationship to electromyographic activity and post-exercise hyperemia

The purpose of this study was to examine the association among electromyographic (EMG) activity, recovery blood flow, and the magnitude of the autonomic adjustments to rhythmic exercise in humans. To accomplish this, 10 healthy subjects (aged 23-37 y) performed rhythmic handgrip exercise for 2 min at 5, 15, 25, 40, and 60% of maximal voluntary force. Heart rate and arterial blood pressure were measured at rest (control), during each level of exercise, and for 2 min following exercise (recovery). The rectified, filtered EMG activity of the exercising forearm was measured continuously during each level of exercise and was used as an index of the level of central command. Post-exercise hyperemia was calculated as the difference between the control and the average recovery (2 min) forearm blood flows (venous occlusion plethysmography) and was examined as a possible index of the stimulus for muscle chemoreflex activation. Heart rate, arterial pressure, forearm EMG activity, and post-exercise hyperemia all increased progressively with increasing exercise intensity. The magnitudes of the increases in heart rate and arterial pressure from control to exercise were directly related to both the level of EMG activity and the degree of post-exercise hyperemia across the five exercise intensities (delta heart rate vs EMG activity: r = 0.99; delta arterial pressure vs EMG activity: r = 0.99; delta heart rate vs hyperemia: r = 0.99; and delta arterial pressure vs hyperemia: r = 0.98; all p less than 0.01). Furthermore, the level of EMG activity was directly related (r = 0.99) to the corresponding degree of hyperemia.(ABSTRACT TRUNCATED AT 250 WORDS)     

Improving elbow torque output of stroke patients with assistive torque controlled by EMG signals

This paper develops an assistive torque system which uses homogeneic surface electromyogram (EMG) signals to improve the elbow torque capability of stroke patients by applying an external time-varying assistive torque. In determining the magnitude of the torque to apply, the incorporated assistive torque algorithm considers the difference between the weighted biceps and triceps EMG signals such that the applied torque is proportional to the effort supplied voluntarily by the user. The overall stability of the assistive system is enhanced by the incorporation of a nonlinear damping element within the control algorithm which mimics the physiological damping of the elbow joint and the co-contraction between the biceps and triceps. Adaptive filtering of the control signal is employed to achieve a balance between the bandwidth and the system adaptability so as to ensure a smooth assistive torque output. The innovative control algorithm enables the provision of an assistive system whose operation is both natural to use and simple to learn. The effectiveness of the proposed assistive system in assisting elbow movement performance is investigated in a series of tests involving five stroke patients and five able-bodied individuals. The results confirm the ability of the system to assist all of the subjects in performing a number of reaching and tracking tasks with reduced effort and with no sacrifice in elbow movement performance.     

Slowed respiration training

A method of slowed respiration rate (RR) training is described that uses visual feedback of the respiratory cycle. Subjects assigned to the slowed RR training procedure were compared with subjects assigned to either a traditional frontal electromyographic (EMG) biofeedback condition or a control condition where no feedback was presented. RR, frontal EMG, heart rate, digital temperature, and skin conductance level were monitored simultaneously. The results indicated that RR training was effective in reducing RR, that RR training had little systematic effect on the other physiological variables, and the frontal EMG procedure did not in itself reduce RR. The advantages of the current methodological approach and the importance of respiration training were discussed along with a literature review. The relationship between RR training and the complexities of respiratory phenomena was discussed, as well as ways that future research using this method may help clarify current issues within respiration training.This research was supported by grant No. 2-S06RR08038-19 from the National Institutes of Health. The able work of Nora Barker and Robert Longoria is acknowledged, who served as biofeedback technicians.     

Effects of an EMG biofeedback relaxation program on the control of diabetes

It was hypothesized that EMG biofeedback relaxation training, applied to a diabetic patient, would result in a decreased level of insulin with fewer episodes of ketoacidosis. A 20-year-old female, diabetic since age nine, kept daily records of insulin doses and rated herself on an emotionality and a diabetic scale. A full-semester baseline was taken. This was followed by a semester-long training program during which the patient practiced relaxing her frontalis muscle with a portable EMG feedback unit which produced a geiger-counter-like click feedback. A cassette-tape series was used along with the portable EMG. The patient was encouraged to practice twice each day and to attempt to maintain a relaxed state even when not in the practice situation. The daily use of the portable unit was terminated at the end of the semester. In addition, the patient ceased practicing twice daily with the cassette tape. Daily insulin averaged 85 units for the six-week baseline and 59 for the final six weeks of the training period. Moreover, at the end of the training period the average dose had reached 43 units. During the training period the patient rated herself as decreasing in emotionality and in diabetic fluctuations.Supported by the National Institute of Mental Health Grant MH-15596.     

Real-Time Classification of EMG Myo Armband Data Using Support Vector Machine

ObjectivesThis study investigates the performance of the Support Vector Machine (SVM) to classify non-real-time and real-time EMG signals. The study also compares training performance using personalized and generalized data from all subjects. Thus, an idea about the data sets to be used in the training of the real-time classification model has been put forward. In addition, real-time classification results were obtained for ten days, and it was observed how training oneself would affect the classification results.Material and methods:EMG data were acquired for 7 hand gestures from 8 healthy subjects to create the data set: fist, fingers spread, wave-in, wave-out, pronation, supination, and rest. Subjects repeated each gesture 30 times. The Myo armband with 8 dry surface electrodes was used for data acquisition.Results14 features of the EMG signals have been extracted and non-real-time classification has been made for each feature; the highest accuracy of 96.38% was obtained using root mean square (RMS) and integrated EMG features. Three (3) kernel functions of SVM were tested in non-real-time classification and the highest accuracy was obtained with Cubic SVM using 3rd order polynomial. For this reason, Cubic SVM was used for real-time classification using the features that gave the best results in non-real-time classification. A subject repeated the gestures and real-time classification was performed. The highest accuracy of 99.05% was obtained with the mean absolute value (MAV) feature. The real-time classification was undertaken on eight subjects using the MAV feature's best performance with an average accuracy of 95.83% using the personalized data set and 91.79% using the generalized data set.ConclusionThe greatest accuracy is obtained by training the classifier with the subject's own data. Thus, it can be said that EMG signals are personal, just like fingerprints and retina. In addition, as a result, the tests repeated for 10 days showed the repeatability of the activation of the relevant muscle set and the training takes place and how this can be applied to those who will use prosthetic hands to obtain certain gestures.     

Identification of isometric contractions based on High Density EMG maps

Identification of motion intention and muscle activation strategy is necessary to control human–machine interfaces like prostheses or orthoses, as well as other rehabilitation devices, games and computer-based training programs. Pattern recognition from sEMG signals has been extensively investigated in the last decades, however, most of the studies did not take into account different strengths and EMG distributions associated to the intended task. The identification of such quantities could be beneficial for the training of the subject or the control of assistive devices. Recent studies have shown the need to improve pattern-recognition classification by reducing sensitivity to changes in the exerted strength, muscle-electrode shifts and bad contacts. Surface High Density EMG (HD-EMG) obtained from 2-dimensional arrays can provide much more information than electrode pairs for inferring not only motion intention but also the strategy adopted to distribute the load between muscles as well as changes in the spatial distribution of motor unit action potentials within a single muscle because of it.The objectives of this study were: (a) the automatic identification of four isometric motor tasks associated with the degrees of freedom of the forearm: flexion–extension and supination–pronation and (b) the differentiation among levels of voluntary contraction at low-medium efforts. For this purpose, monopolar HD-EMG maps were obtained from five muscles of the upper-limb in healthy subjects. An original classifier is proposed, based on: (1) Two steps linear discriminant analysis of the EMG information for each type of contraction, and (2) features extracted from HD-EMG maps and related to its intensity and distribution in the 2D space. The classifier was trained and tested with different effort levels. Spatial distribution-based features by themselves are not sufficient to classify the type of task or the effort level with an acceptable accuracy; however, when calculated with the “isolated masses” method proposed in this study and combined with intensity-base features, the performance of the classifier is improved. The classifier is capable of identifying the tasks even at 10% of Maximum Voluntary Contraction, in the range of effort level developed by patients with neuromuscular disorders, showing that intention end effort of motion can be estimated from HD-EMG maps and applied in rehabilitation.     

Biofeedback,relaxation training,and music: Homeostasis for coping with stress

This study compared the efficacy of five relaxation training procedures, four of which employed EMG auditory feedback: (1) biofeedback only (BF), (2) autogenic training phrases (ATP), (3) music (MU), (4) autogenic training phrases and music (ATP & MU), and (5) a control group, in developing self-regulation of a cultivated low arousal state as a countermeasure to tensed muscular reaction to stressful imagery. Twenty subjects established a pre- and posttraining frontalis region EMG biofeedback baseline measurement. Sixteen subjects were assigned at random to the 25-minute taped relaxation training procedure. After eight training sessions (4 weeks), MU and ATP & MU groups achieved highly significant differences when compared with the control group. The ATP & MU group attained the lowest postbaseline arousal level measured by the EMG. EMG as a physiological measure for transfer of training functioned well in detecting the psychophysiological affect of stressful imagery.This report is based on a thesis submitted in partial fulfillment of the requirements for the Master of Arts in Psychology degree by the author. The author extends his gratitude to Dr. Theodore Steiner, Dr. Paul Eskildsen, and Dr. Frank Hovell, who served on the committee, and to Rosemary Kolentus, for her help with this article.     

Change Mechanisms Associated with Combined Relaxation/EMG Biofeedback Training for Chronic Tension Headache

Therapeutic mechanisms hypothesized to underlie improvements in tension headache activity achieved with combined relaxation and eleclromyographic (EMG) biofeedback therapy were examined. These therapeutic mechanisms included (1) changes in EMG activity in frontal and trapezii muscles, (2) changes in central pain modulation as indexed by the duration of the second exteroceptive silent period (ES2), and (3) changes in headache locus of control and self-efficacy. Forty-four young adults with chronic tension-type headaches were assigned either to six sessions of relaxation and EMG biofeedback training (N = 30) or to an assessment only control group (N = 14) that required three assessment sessions. Measures of self-efficacy and locus of control were collected at pre- and posttreatment, and ES2 was evaluated at the beginning and end of the first, third, and lost session. EMG was monitored before, during, and following training trials. Relaxation/EMG biofeedback training effectively reduced headache activity: 51.7% of subjects who received relaxation/biofeedback therapy recorded at least a 50% reduction in headache activity following treatment, while controls failed to improve on any measure. Improvements in headache activity in treated subjects were correlated with increases in self-efficacy induced by biofeedback training but not with changes in EMG activity or in ES2 durations. These results provide additional support for the hypothesis that cognitive changes underlie the effectiveness of relaxation and biofeedback therapies, at least in young adult tension-type headache sufferers.     

Cognitive self-control factors in EMG biofeedback

This study investigated the efficacy of manipulation of cognitive self-control expectancy in EMG biofeedback training. It was predicted that a treatment procedure, which includes a positive-cognitive stage that establishes and reinforces a positive self-control belief system and also includes a training stage in EMG biofeedback, will be more effective in achieving a reduction in EMG activity than a treatment procedure which includes a negative-cognitive stage and which also includes ambiguous features prior to training and a treatment approach solely concerned with training. The study consisted of four groups with 10 subjects in each. In one group, expectation for inner control ability was created prior to actual training in reducing EMG activity. In the second group, expectation for negative self-control ability was created prior to EMG training. The third group only underwent the actual training in EMG. The fourth group served as a control group. The results show that the positive-cognitive self-control group was significantly more effective in reducing muscle activity than the other groups.     

Differential shaping of EEG theta rhythms

Heart rate, EEG, frontal EMG, and forearm EMG were recorded in 20 subjects for 3 baseline, 8 feedback, and 2 postbaseline sessions in order to compare two biofeedback methods of teaching subjects to increase theta EEG activity. Subjects were divided into high- and low-EMG groups. Five high-EMG subjects, and 5 low-EMG subjects then received 8 sessions of strictly theta feedback. The remaining 10 subjects, 5 from the high-EMG group, and 5 from the low-EMG group, received a “graduated” training which involved shaping the target response. This procedure consisted of 4 initial sessions of EMG feedback, followed by a second phase consisting of 4 sessions of theta feedback. Results showed a clear relationship between subjects' baseline frontal EMG levels and the effect of the training methods. Although subjects with high-EMG baseline increased their theta output only with the two-phase training, subjects with low-EMG baseline levels performed better when given theta feedback only. This result shows not only that amounts of theta can be reliably increased, but that training techniques should be adapted to the physiological characteristics of the individual—in this case, baseline levels of frontal EMG levels.     

Changes in muscle activation can prolong the endurance time of a submaximal isometric contraction in humans.

Fourteen young subjects (7 men and 7 women) performed a fatiguing isometric contraction with the elbow flexor muscles at 20% of maximal voluntary contraction (MVC) force on three occasions. Endurance time for session 3 [1,718 +/- 1,189 (SD) s] was longer than for session 1 (1,225 +/- 683 s) and session 2 (1,410 +/- 977 s). Five men and four women increased endurance time between session 1 and 3 by 60 +/- 28% (responders), whereas two men and three women did not (-3 +/- 11%; nonresponders). The MVC force was similar for the responders and nonresponders, both before and after the fatiguing contraction. Fatiguing contractions were characterized by an increase in the electromyogram (EMG) amplitude and number of bursts during the fatiguing contractions. The responders achieved a similar level of EMG at exhaustion but a reduced rate of increase in the EMG across sessions. The rate of increase in EMG across sessions declined for the nonresponders, but it remained greater than that of the responders. The increase in burst rate during the contractions declined across sessions with a negative relation between burst rate and endurance time (r = -0.42). Normalized force fluctuations increased during the fatiguing contractions, and there was a positive relation (r = 0.60) between the force fluctuations and burst rate. Changes in mean arterial pressure and heart rate during the fatiguing contraction were similar for the responders and nonresponders across the three sessions. The results indicate that those subjects who increased the endurance time of a submaximal contraction across three sessions did so by altering the level and pattern of muscle activation.