EMG signals characterization in three states of contraction by fuzzy network and feature extraction

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Mokhlesabadifarahani, Bita

2015-01-01

Neuro-muscular and musculoskeletal disorders and injuries highly affect the life style and the motion abilities of an individual. This brief highlights a systematic method for detection of the level of muscle power declining in musculoskeletal and Neuro-muscular disorders. The neuro-fuzzy system is trained with 70 percent of the recorded Electromyography (EMG) cut off window and then used for classification and modeling purposes. The neuro-fuzzy classifier is validated in comparison to some other well-known classifiers in classification of the recorded EMG signals with the three states of contractions corresponding to the extracted features. Different structures of the neuro-fuzzy classifier are also comparatively analyzed to find the optimum structure of the classifier used.

siGnum: graphical user interface for EMG signal analysis.

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Kaur, Manvinder; Mathur, Shilpi; Bhatia, Dinesh; Verma, Suresh

2015-01-01

Electromyography (EMG) signals that represent the electrical activity of muscles can be used for various clinical and biomedical applications. These are complicated and highly varying signals that are dependent on anatomical location and physiological properties of the muscles. EMG signals acquired from the muscles require advanced methods for detection, decomposition and processing. This paper proposes a novel Graphical User Interface (GUI) siGnum developed in MATLAB that will apply efficient and effective techniques on processing of the raw EMG signals and decompose it in a simpler manner. It could be used independent of MATLAB software by employing a deploy tool. This would enable researcher's to gain good understanding of EMG signal and its analysis procedures that can be utilized for more powerful, flexible and efficient applications in near future.

Emg Signal Analysis of Healthy and Neuropathic Individuals

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Gupta, Ashutosh; Sayed, Tabassum; Garg, Ridhi; Shreyam, Richa

2017-08-01

Electromyography is a method to evaluate levels of muscle activity. When a muscle contracts, an action potential is generated and this circulates along the muscular fibers. In electromyography, electrodes are connected to the skin and the electrical activity of muscles is measured and graph is plotted. The surface EMG signals picked up during the muscular activity are interfaced with a system. The EMG signals from individual suffering from Neuropathy and healthy individual, so obtained, are processed and analyzed using signal processing techniques. This project includes the investigation and interpretation of EMG signals of healthy and Neuropathic individuals using MATLAB. The prospective use of this study is in developing the prosthetic device for the people with Neuropathic disability.

A Variance Distribution Model of Surface EMG Signals Based on Inverse Gamma Distribution.

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Hayashi, Hideaki; Furui, Akira; Kurita, Yuichi; Tsuji, Toshio

2017-11-01

Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this variance. Variance distribution estimation based on marginal likelihood maximization is also outlined in this paper. The procedure can be approximated using rectified and smoothed EMG signals, thereby allowing the determination of distribution parameters in real time at low computational cost. Results: A simulation experiment was performed to evaluate the accuracy of distribution estimation using artificially generated EMG signals, with results demonstrating that the proposed model's accuracy is higher than that of maximum-likelihood-based estimation. Analysis of variance distribution using real EMG data also suggested a relationship between variance distribution and signal-dependent noise. Conclusion: The study reported here was conducted to examine the performance of a proposed surface EMG model capable of representing variance distribution and a related distribution parameter estimation method. Experiments using artificial and real EMG data demonstrated the validity of the model. Significance: Variance distribution estimated using the proposed model exhibits potential in the estimation of muscle force. Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this

Seizure detection algorithms based on EMG signals

DEFF Research Database (Denmark)

Conradsen, Isa

Background: the currently used non-invasive seizure detection methods are not reliable. Muscle fibers are directly connected to the nerves, whereby electric signals are generated during activity. Therefore, an alarm system on electromyography (EMG) signals is a theoretical possibility. Objective...... on the amplitude of the signal. The other algorithm was based on information of the signal in the frequency domain, and it focused on synchronisation of the electrical activity in a single muscle during the seizure. Results: The amplitude-based algorithm reliably detected seizures in 2 of the patients, while...... the frequency-based algorithm was efficient for detecting the seizures in the third patient. Conclusion: Our results suggest that EMG signals could be used to develop an automatic seizuredetection system. However, different patients might require different types of algorithms /approaches....

Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography

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Siu, Ho Chit; Shah, Julie A.; Stirling, Leia A.

2016-01-01

Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We used the results of an experiment incorporating grasp and release actions with object contact to develop an intent-recognition system based on Gaussian mixture models (GMM) and continuous-emission hidden Markov models (HMM) of sEMG data. We tested this system with data collected from 16 individuals using a forearm band with distributed sEMG sensors. The data contain trials with shifted band alignments to assess robustness to sensor placement. This study evaluated and found that pattern-recognition-based methods could classify transient anticipatory sEMG signals in the presence of shifted sensor placement and object contact. With the best-performing classifier, the effect of label lengths in the training data was also examined. A mean classification accuracy of 75.96% was achieved through a unigram HMM method with five mixture components. Classification accuracy on different sub-movements was found to be limited by the length of the shortest sub-movement, which means that shorter sub-movements within dynamic sequences require larger training sets to be classified correctly. This classification of user intent is a potential control mechanism for a dynamic grasping task involving user contact with external objects and noise. Further work is required to test its performance as part of an exoskeleton controller, which involves contact with actuated external surfaces. PMID:27792155

Design of microcontroller-based EMG and the analysis of EMG signals.

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Güler, Nihal Fatma; Hardalaç, Firat

2002-04-01

In this work, a microcontroller-based EMG designed and tested on 40 patients. When the patients are in rest, the fast Fourier transform (FFT) analysis was applied to EMG signals recorded from right leg peroneal region. The histograms are constructed from the results of the FFT analysis. The analysis results shows that the amplitude of fibrillation potential of the muscle fiber of 30 patients measured from peroneal region is low and the duration is short. This is the reason why the motor nerves degenerated and 10 patients were found to be healthy.

Identification of motion from multi-channel EMG signals for control of prosthetic hand

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Geethanjali, P.; Ray, K.K.

2011-01-01

Full text: The authors in this paper propose an effective and efficient pattern recognition technique from four channel electromyogram (EMG) signals for control of multifunction prosthetic hand. Time domain features such as mean absolute value, number of zero crossings, number of slope sign changes and waveform length are considered for pattern recognition. The patterns are classified using simple logistic regression (SLR) technique and decision tree (DT) using J48 algorithm. In this study six specific hand and wrist motions are identified from the EMG signals obtained from ten different able-bodied. By considering relevant dominant features for pattern recognition, the processing time as well as memory space of the SLR and DT classifiers is found to be less in comparison with neural network (NN), k-nearest neighbour model 1 (kNN Model-1), k-nearest neighbour model 2 (kNN-Model-2) and linear discriminant analysis. The classification accuracy of SLR classifier is found to be 91 ± 1.9%. (author)

Intelligent Noise Removal from EMG Signal Using Focused Time-Lagged Recurrent Neural Network

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S. N. Kale

2009-01-01

Full Text Available Electromyography (EMG signals can be used for clinical/biomedical application and modern human computer interaction. EMG signals acquire noise while traveling through tissue, inherent noise in electronics equipment, ambient noise, and so forth. ANN approach is studied for reduction of noise in EMG signal. In this paper, it is shown that Focused Time-Lagged Recurrent Neural Network (FTLRNN can elegantly solve to reduce the noise from EMG signal. After rigorous computer simulations, authors developed an optimal FTLRNN model, which removes the noise from the EMG signal. Results show that the proposed optimal FTLRNN model has an MSE (Mean Square Error as low as 0.000067 and 0.000048, correlation coefficient as high as 0.99950 and 0.99939 for noise signal and EMG signal, respectively, when validated on the test dataset. It is also noticed that the output of the estimated FTLRNN model closely follows the real one. This network is indeed robust as EMG signal tolerates the noise variance from 0.1 to 0.4 for uniform noise and 0.30 for Gaussian noise. It is clear that the training of the network is independent of specific partitioning of dataset. It is seen that the performance of the proposed FTLRNN model clearly outperforms the best Multilayer perceptron (MLP and Radial Basis Function NN (RBF models. The simple NN model such as the FTLRNN with single-hidden layer can be employed to remove noise from EMG signal.

Detection of driving fatigue by using noncontact EMG and ECG signals measurement system.

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Fu, Rongrong; Wang, Hong

2014-05-01

Driver fatigue can be detected by constructing a discriminant mode using some features obtained from physiological signals. There exist two major challenges of this kind of methods. One is how to collect physiological signals from subjects while they are driving without any interruption. The other is to find features of physiological signals that are of corresponding change with the loss of attention caused by driver fatigue. Driving fatigue is detected based on the study of surface electromyography (EMG) and electrocardiograph (ECG) during the driving period. The noncontact data acquisition system was used to collect physiological signals from the biceps femoris of each subject to tackle the first challenge. Fast independent component analysis (FastICA) and digital filter were utilized to process the original signals. Based on the statistical analysis results given by Kolmogorov-Smirnov Z test, the peak factor of EMG (p fatigue of drivers. The discriminant criterion of fatigue was obtained from the training samples by using Mahalanobis distance, and then the average classification accuracy was given by 10-fold cross-validation. The results showed that the method proposed in this paper can give well performance in distinguishing the normal state and fatigue state. The noncontact, onboard vehicle drivers' fatigue detection system was developed to reduce fatigue-related risks.

Preferred sensor sites for surface EMG signal decomposition

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Zaheer, Farah; Roy, Serge H; De Luca, Carlo J

2012-01-01

Technologies for decomposing the electromyographic (EMG) signal into its constituent motor unit action potential trains have become more practical by the advent of a non-invasive methodology using surface EMG (sEMG) sensors placed on the skin above the muscle of interest (De Luca et al 2006 J. Neurophysiol. 96 1646–57 and Nawab et al 2010 Clin. Neurophysiol. 121 1602–15). This advancement has widespread appeal among researchers and clinicians because of the ease of use, reduced risk of infection, and the greater number of motor unit action potential trains obtained compared to needle sensor techniques. In this study we investigated the influence of the sensor site on the number of identified motor unit action potential trains in six lower limb muscles and one upper limb muscle with the intent of locating preferred sensor sites that provided the greatest number of decomposed motor unit action potential trains, or motor unit yield. Sensor sites rendered varying motor unit yields throughout the surface of a muscle. The preferred sites were located between the center and the tendinous areas of the muscle. The motor unit yield was positively correlated with the signal-to-noise ratio of the detected sEMG. The signal-to-noise ratio was inversely related to the thickness of the tissue between the sensor and the muscle fibers. A signal-to-noise ratio of 3 was found to be the minimum required to obtain a reliable motor unit yield. (paper)

sEMG-Based Gesture Recognition with Convolution Neural Networks

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Zhen Ding

2018-06-01

Full Text Available The traditional classification methods for limb motion recognition based on sEMG have been deeply researched and shown promising results. However, information loss during feature extraction reduces the recognition accuracy. To obtain higher accuracy, the deep learning method was introduced. In this paper, we propose a parallel multiple-scale convolution architecture. Compared with the state-of-art methods, the proposed architecture fully considers the characteristics of the sEMG signal. Larger sizes of kernel filter than commonly used in other CNN-based hand recognition methods are adopted. Meanwhile, the characteristics of the sEMG signal, that is, muscle independence, is considered when designing the architecture. All the classification methods were evaluated on the NinaPro database. The results show that the proposed architecture has the highest recognition accuracy. Furthermore, the results indicate that parallel multiple-scale convolution architecture with larger size of kernel filter and considering muscle independence can significantly increase the classification accuracy.

A method for discrimination of noise and EMG signal regions recorded during rhythmic behaviors.

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Ying, Rex; Wall, Christine E

2016-12-08

Analyses of muscular activity during rhythmic behaviors provide critical data for biomechanical studies. Electrical potentials measured from muscles using electromyography (EMG) require discrimination of noise regions as the first step in analysis. An experienced analyst can accurately identify the onset and offset of EMG but this process takes hours to analyze a short (10-15s) record of rhythmic EMG bursts. Existing computational techniques reduce this time but have limitations. These include a universal threshold for delimiting noise regions (i.e., a single signal value for identifying the EMG signal onset and offset), pre-processing using wide time intervals that dampen sensitivity for EMG signal characteristics, poor performance when a low frequency component (e.g., DC offset) is present, and high computational complexity leading to lack of time efficiency. We present a new statistical method and MATLAB script (EMG-Extractor) that includes an adaptive algorithm to discriminate noise regions from EMG that avoids these limitations and allows for multi-channel datasets to be processed. We evaluate the EMG-Extractor with EMG data on mammalian jaw-adductor muscles during mastication, a rhythmic behavior typified by low amplitude onsets/offsets and complex signal pattern. The EMG-Extractor consistently and accurately distinguishes noise from EMG in a manner similar to that of an experienced analyst. It outputs the raw EMG signal region in a form ready for further analysis. Copyright © 2016 Elsevier Ltd. All rights reserved.

The effect of time on EMG classification of hand motions in able-bodied and transradial amputees

DEFF Research Database (Denmark)

Waris, Asim; Niazi, Imran Khan; Jamil, Mohsin

2018-01-01

While several studies have demonstrated the short-term performance of pattern recognition systems, long-term investigations are very limited. In this study, we investigated changes in classification performance over time. Ten able-bodied individuals and six amputees took part in this study. EMG s...... difference between training and testing day increases. Furthermore, for iEMG, performance in amputees was directly proportional to the size of the residual limb.......While several studies have demonstrated the short-term performance of pattern recognition systems, long-term investigations are very limited. In this study, we investigated changes in classification performance over time. Ten able-bodied individuals and six amputees took part in this study. EMG...... was computed for all possible combinations between the days. For all subjects, surface sEMG (7.2 ± 7.6%), iEMG (11.9 ± 9.1%) and cEMG (4.6 ± 4.8%) were significantly different (P 

Long-term surface EMG monitoring using K-means clustering and compressive sensing

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Balouchestani, Mohammadreza; Krishnan, Sridhar

2015-05-01

In this work, we present an advanced K-means clustering algorithm based on Compressed Sensing theory (CS) in combination with the K-Singular Value Decomposition (K-SVD) method for Clustering of long-term recording of surface Electromyography (sEMG) signals. The long-term monitoring of sEMG signals aims at recording of the electrical activity produced by muscles which are very useful procedure for treatment and diagnostic purposes as well as for detection of various pathologies. The proposed algorithm is examined for three scenarios of sEMG signals including healthy person (sEMG-Healthy), a patient with myopathy (sEMG-Myopathy), and a patient with neuropathy (sEMG-Neuropathr), respectively. The proposed algorithm can easily scan large sEMG datasets of long-term sEMG recording. We test the proposed algorithm with Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) dimensionality reduction methods. Then, the output of the proposed algorithm is fed to K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers in order to calclute the clustering performance. The proposed algorithm achieves a classification accuracy of 99.22%. This ability allows reducing 17% of Average Classification Error (ACE), 9% of Training Error (TE), and 18% of Root Mean Square Error (RMSE). The proposed algorithm also reduces 14% clustering energy consumption compared to the existing K-Means clustering algorithm.

A model for generating Surface EMG signal of m. Tibialis Anterior.

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Siddiqi, Ariba; Kumar, Dinesh; Arjunan, Sridhar P

2014-01-01

A model that simulates surface electromyogram (sEMG) signal of m. Tibialis Anterior has been developed and tested. This has a firing rate equation that is based on experimental findings. It also has a recruitment threshold that is based on observed statistical distribution. Importantly, it has considered both, slow and fast type which has been distinguished based on their conduction velocity. This model has assumed that the deeper unipennate half of the muscle does not contribute significantly to the potential induced on the surface of the muscle and has approximated the muscle to have parallel structure. The model was validated by comparing the simulated and the experimental sEMG signal recordings. Experiments were conducted on eight subjects who performed isometric dorsiflexion at 10, 20, 30, 50, 75, and 100% maximal voluntary contraction. Normalized root mean square and median frequency of the experimental and simulated EMG signal were computed and the slopes of the linearity with the force were statistically analyzed. The gradients were found to be similar (p>0.05) for both experimental and simulated sEMG signal, validating the proposed model.

Simultaneous Force Regression and Movement Classification of Fingers via Surface EMG within a Unified Bayesian Framework.

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Baldacchino, Tara; Jacobs, William R; Anderson, Sean R; Worden, Keith; Rowson, Jennifer

2018-01-01

This contribution presents a novel methodology for myolectric-based control using surface electromyographic (sEMG) signals recorded during finger movements. A multivariate Bayesian mixture of experts (MoE) model is introduced which provides a powerful method for modeling force regression at the fingertips, while also performing finger movement classification as a by-product of the modeling algorithm. Bayesian inference of the model allows uncertainties to be naturally incorporated into the model structure. This method is tested using data from the publicly released NinaPro database which consists of sEMG recordings for 6 degree-of-freedom force activations for 40 intact subjects. The results demonstrate that the MoE model achieves similar performance compared to the benchmark set by the authors of NinaPro for finger force regression. Additionally, inherent to the Bayesian framework is the inclusion of uncertainty in the model parameters, naturally providing confidence bounds on the force regression predictions. Furthermore, the integrated clustering step allows a detailed investigation into classification of the finger movements, without incurring any extra computational effort. Subsequently, a systematic approach to assessing the importance of the number of electrodes needed for accurate control is performed via sensitivity analysis techniques. A slight degradation in regression performance is observed for a reduced number of electrodes, while classification performance is unaffected.

Adaptive EMG noise reduction in ECG signals using noise level approximation

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Marouf, Mohamed; Saranovac, Lazar

2017-12-01

In this paper the usage of noise level approximation for adaptive Electromyogram (EMG) noise reduction in the Electrocardiogram (ECG) signals is introduced. To achieve the adequate adaptiveness, a translation-invariant noise level approximation is employed. The approximation is done in the form of a guiding signal extracted as an estimation of the signal quality vs. EMG noise. The noise reduction framework is based on a bank of low pass filters. So, the adaptive noise reduction is achieved by selecting the appropriate filter with respect to the guiding signal aiming to obtain the best trade-off between the signal distortion caused by filtering and the signal readability. For the evaluation purposes; both real EMG and artificial noises are used. The tested ECG signals are from the MIT-BIH Arrhythmia Database Directory, while both real and artificial records of EMG noise are added and used in the evaluation process. Firstly, comparison with state of the art methods is conducted to verify the performance of the proposed approach in terms of noise cancellation while preserving the QRS complex waves. Additionally, the signal to noise ratio improvement after the adaptive noise reduction is computed and presented for the proposed method. Finally, the impact of adaptive noise reduction method on QRS complexes detection was studied. The tested signals are delineated using a state of the art method, and the QRS detection improvement for different SNR is presented.

A novel method for EMG decomposition based on matched filters

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Ailton Luiz Dias Siqueira Júnior

Full Text Available Introduction Decomposition of electromyography (EMG signals into the constituent motor unit action potentials (MUAPs can allow for deeper insights into the underlying processes associated with the neuromuscular system. The vast majority of the methods for EMG decomposition found in the literature depend on complex algorithms and specific instrumentation. As an attempt to contribute to solving these issues, we propose a method based on a bank of matched filters for the decomposition of EMG signals. Methods Four main units comprise our method: a bank of matched filters, a peak detector, a motor unit classifier and an overlapping resolution module. The system’s performance was evaluated with simulated and real EMG data. Classification accuracy was measured by comparing the responses of the system with known data from the simulator and with the annotations of a human expert. Results The results show that decomposition of non-overlapping MUAPs can be achieved with up to 99% accuracy for signals with up to 10 active motor units and a signal-to-noise ratio (SNR of 10 dB. For overlapping MUAPs with up to 10 motor units per signal and a SNR of 20 dB, the technique allows for correct classification of approximately 71% of the MUAPs. The method is capable of processing, decomposing and classifying a 50 ms window of data in less than 5 ms using a standard desktop computer. Conclusion This article contributes to the ongoing research on EMG decomposition by describing a novel technique capable of delivering high rates of success by means of a fast algorithm, suggesting its possible use in future real-time embedded applications, such as myoelectric prostheses control and biofeedback systems.

Bilinear modeling of EMG signals to extract user-independent features for multiuser myoelectric interface.

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Matsubara, Takamitsu; Morimoto, Jun

2013-08-01

In this study, we propose a multiuser myoelectric interface that can easily adapt to novel users. When a user performs different motions (e.g., grasping and pinching), different electromyography (EMG) signals are measured. When different users perform the same motion (e.g., grasping), different EMG signals are also measured. Therefore, designing a myoelectric interface that can be used by multiple users to perform multiple motions is difficult. To cope with this problem, we propose for EMG signals a bilinear model that is composed of two linear factors: 1) user dependent and 2) motion dependent. By decomposing the EMG signals into these two factors, the extracted motion-dependent factors can be used as user-independent features. We can construct a motion classifier on the extracted feature space to develop the multiuser interface. For novel users, the proposed adaptation method estimates the user-dependent factor through only a few interactions. The bilinear EMG model with the estimated user-dependent factor can extract the user-independent features from the novel user data. We applied our proposed method to a recognition task of five hand gestures for robotic hand control using four-channel EMG signals measured from subject forearms. Our method resulted in 73% accuracy, which was statistically significantly different from the accuracy of standard nonmultiuser interfaces, as the result of a two-sample t -test at a significance level of 1%.

EOG-sEMG Human Interface for Communication.

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Tamura, Hiroki; Yan, Mingmin; Sakurai, Keiko; Tanno, Koichi

2016-01-01

The aim of this study is to present electrooculogram (EOG) and surface electromyogram (sEMG) signals that can be used as a human-computer interface. Establishing an efficient alternative channel for communication without overt speech and hand movements is important for increasing the quality of life for patients suffering from amyotrophic lateral sclerosis, muscular dystrophy, or other illnesses. In this paper, we propose an EOG-sEMG human-computer interface system for communication using both cross-channels and parallel lines channels on the face with the same electrodes. This system could record EOG and sEMG signals as "dual-modality" for pattern recognition simultaneously. Although as much as 4 patterns could be recognized, dealing with the state of the patients, we only choose two classes (left and right motion) of EOG and two classes (left blink and right blink) of sEMG which are easily to be realized for simulation and monitoring task. From the simulation results, our system achieved four-pattern classification with an accuracy of 95.1%.

Evaluation of jaw and neck muscle activities while chewing using EMG-EMG transfer function and EMG-EMG coherence function analyses in healthy subjects.

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Ishii, Tomohiro; Narita, Noriyuki; Endo, Hiroshi

2016-06-01

This study aims to quantitatively clarify the physiological features in rhythmically coordinated jaw and neck muscle EMG activities while chewing gum using EMG-EMG transfer function and EMG-EMG coherence function analyses in 20 healthy subjects. The chewing side masseter muscle EMG signal was used as the reference signal, while the other jaw (non-chewing side masseter muscle, bilateral anterior temporal muscles, and bilateral anterior digastric muscles) and neck muscle (bilateral sternocleidomastoid muscles) EMG signals were used as the examined signals in EMG-EMG transfer function and EMG-EMG coherence function analyses. Chewing-related jaw and neck muscle activities were aggregated in the first peak of the power spectrum in rhythmic chewing. The gain in the peak frequency represented the power relationships between jaw and neck muscle activities during rhythmic chewing. The phase in the peak frequency represented the temporal relationships between the jaw and neck muscle activities, while the non-chewing side neck muscle presented a broad range of distributions across jaw closing and opening phases. Coherence in the peak frequency represented the synergistic features in bilateral jaw closing muscles and chewing side neck muscle activities. The coherence and phase in non-chewing side neck muscle activities exhibited a significant negative correlation. From above, the bilateral coordination between the jaw and neck muscle activities is estimated while chewing when the non-chewing side neck muscle is synchronously activated with the jaw closing muscles, while the unilateral coordination is estimated when the non-chewing side neck muscle is irregularly activated in the jaw opening phase. Thus, the occurrence of bilateral or unilateral coordinated features in the jaw and neck muscle activities may correspond to the phase characteristics in the non-chewing side neck muscle activities during rhythmical chewing. Considering these novel findings in healthy subjects, EMG-EMG

Intermuscular Coherence Between Surface EMG Signals Is Higher for Monopolar Compared to Bipolar Electrode Configurations

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Maurice Mohr

2018-05-01

Full Text Available Introduction: The vasti muscles have to work in concert to control knee joint motion during movements like walking, running, or squatting. Coherence analysis between surface electromyography (EMG signals is a common technique to study muscle synchronization during such movements and gain insight into strategies of the central nervous system to optimize neuromuscular performance. However, different assessment methods related to EMG data acquisition, e.g., different electrode configurations or amplifier technologies, have produced inconsistent observations. Therefore, the aim of this study was to elucidate the effect of different EMG acquisition techniques (monopolar vs. bipolar electrode configuration, potential vs. current amplifier on the magnitude, reliability, and sensitivity of intermuscular coherence between two vasti muscles during stable and unstable squatting exercises.Methods: Surface EMG signals from vastus lateralis (VL and medialis (VM were obtained from eighteen adults while performing series of stable und unstable bipedal squats. The EMG signals were acquired using three different recording techniques: (1 Bipolar with a potential amplifier, (2 monopolar with a potential amplifier, and (3 monopolar electrodes with a current amplifier. VL-VM coherence between the respective raw EMG signals was determined during two trials of stable squatting and one trial of unstable squatting to compare the coherence magnitude, reliability, and sensitivity between EMG recording techniques.Results: VL-VM coherence was about twice as high for monopolar recordings compared to bipolar recordings for all squatting exercises while coherence was similar between monopolar potential and current recordings. Reliability measures were comparable between recording systems while the sensitivity to an increase in intermuscular coherence during unstable vs. stable squatting was lowest for the monopolar potential system.Discussion and Conclusion: The choice of

User adaptation in long-term, open-loop myoelectric training: implications for EMG pattern recognition in prosthesis control

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He, Jiayuan; Zhang, Dingguo; Jiang, Ning; Sheng, Xinjun; Farina, Dario; Zhu, Xiangyang

2015-08-01

Objective. Recent studies have reported that the classification performance of electromyographic (EMG) signals degrades over time without proper classification retraining. This problem is relevant for the applications of EMG pattern recognition in the control of active prostheses. Approach. In this study we investigated the changes in EMG classification performance over 11 consecutive days in eight able-bodied subjects and two amputees. Main results. It was observed that, when the classifier was trained on data from one day and tested on data from the following day, the classification error decreased exponentially but plateaued after four days for able-bodied subjects and six to nine days for amputees. The between-day performance became gradually closer to the corresponding within-day performance. Significance. These results indicate that the relative changes in EMG signal features over time become progressively smaller when the number of days during which the subjects perform the pre-defined motions are increased. The performance of the motor tasks is thus more consistent over time, resulting in more repeatable EMG patterns, even if the subjects do not have any external feedback on their performance. The learning curves for both able-bodied subjects and subjects with limb deficiencies could be modeled as an exponential function. These results provide important insights into the user adaptation characteristics during practical long-term myoelectric control applications, with implications for the design of an adaptive pattern recognition system.

Linear correlation between fractal dimension of surface EMG signal from Rectus Femoris and height of vertical jump

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Ancillao, Andrea; Galli, Manuela; Rigoldi, Chiara; Albertini, Giorgio

2014-01-01

Fractal dimension was demonstrated to be able to characterize the complexity of biological signals. The EMG time series are well known to have a complex behavior and some other studies already tried to characterize these signals by their fractal dimension. This paper is aimed at studying the correlation between the fractal dimension of surface EMG signal recorded over Rectus Femoris muscles during a vertical jump and the height reached in that jump. Healthy subjects performed vertical jumps at different heights. Surface EMG from Rectus Femoris was recorded and the height of each jump was measured by an optoelectronic motion capture system. Fractal dimension of sEMG was computed and the correlation between fractal dimension and eight of the jump was studied. Linear regression analysis showed a very high correlation coefficient between the fractal dimension and the height of the jump for all the subjects. The results of this study show that the fractal dimension is able to characterize the EMG signal and it can be related to the performance of the jump. Fractal dimension is therefore an useful tool for EMG interpretation

EMG Versus Torque Control of Human-Machine Systems: Equalizing Control Signal Variability Does not Equalize Error or Uncertainty.

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Johnson, Reva E; Kording, Konrad P; Hargrove, Levi J; Sensinger, Jonathon W

2017-06-01

In this paper we asked the question: if we artificially raise the variability of torque control signals to match that of EMG, do subjects make similar errors and have similar uncertainty about their movements? We answered this question using two experiments in which subjects used three different control signals: torque, torque+noise, and EMG. First, we measured error on a simple target-hitting task in which subjects received visual feedback only at the end of their movements. We found that even when the signal-to-noise ratio was equal across EMG and torque+noise control signals, EMG resulted in larger errors. Second, we quantified uncertainty by measuring the just-noticeable difference of a visual perturbation. We found that for equal errors, EMG resulted in higher movement uncertainty than both torque and torque+noise. The differences suggest that performance and confidence are influenced by more than just the noisiness of the control signal, and suggest that other factors, such as the user's ability to incorporate feedback and develop accurate internal models, also have significant impacts on the performance and confidence of a person's actions. We theorize that users have difficulty distinguishing between random and systematic errors for EMG control, and future work should examine in more detail the types of errors made with EMG control.

Electromyography (EMG) signal recognition using combined discrete wavelet transform based adaptive neuro-fuzzy inference systems (ANFIS)

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Arozi, Moh; Putri, Farika T.; Ariyanto, Mochammad; Khusnul Ari, M.; Munadi, Setiawan, Joga D.

2017-01-01

People with disabilities are increasing from year to year either due to congenital factors, sickness, accident factors and war. One form of disability is the case of interruptions of hand function. The condition requires and encourages the search for solutions in the form of creating an artificial hand with the ability as a human hand. The development of science in the field of neuroscience currently allows the use of electromyography (EMG) to control the motion of artificial prosthetic hand into the necessary use of EMG as an input signal to control artificial prosthetic hand. This study is the beginning of a significant research planned in the development of artificial prosthetic hand with EMG signal input. This initial research focused on the study of EMG signal recognition. Preliminary results show that the EMG signal recognition using combined discrete wavelet transform and Adaptive Neuro-Fuzzy Inference System (ANFIS) produces accuracy 98.3 % for training and 98.51% for testing. Thus the results can be used as an input signal for Simulink block diagram of a prosthetic hand that will be developed on next study. The research will proceed with the construction of artificial prosthetic hand along with Simulink program controlling and integrating everything into one system.

Processing of Natural Signals like EMG for Person Identification using NUFB-GMM

OpenAIRE

Suresh M; P G Krishnamohan; Mallikarjun S Holi

2014-01-01

Physiological signals like Electrocardiogram(ECG) and Electroencephalogram(EEG), including deoxyribonucleic acid(DNA) are person specific and distinct for different persons. The motor unit firing pattern, motor unit recruitment order and characteristics of muscle changing from person to person, and therefore Electromyogram (EMG) can be used for person identification. EMG records obtained from a single channel data acquisition system are used to develop person identification system. Non-unifor...

Generating Human-Like Velocity-Adapted Jumping Gait from sEMG Signals for Bionic Leg’s Control

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Weiwei Yu

2017-01-01

Full Text Available In the case of dynamic motion such as jumping, an important fact in sEMG (surface Electromyogram signal based control on exoskeletons, myoelectric prostheses, and rehabilitation gait is that multichannel sEMG signals contain mass data and vary greatly with time, which makes it difficult to generate compliant gait. Inspired by the fact that muscle synergies leading to dimensionality reduction may simplify motor control and learning, this paper proposes a new approach to generate flexible gait based on muscle synergies extracted from sEMG signal. Two questions were discussed and solved, the first one concerning whether the same set of muscle synergies can explain the different phases of hopping movement with various velocities. The second one is about how to generate self-adapted gait with muscle synergies while alleviating model sensitivity to sEMG transient changes. From the experimental results, the proposed method shows good performance both in accuracy and in robustness for producing velocity-adapted vertical jumping gait. The method discussed in this paper provides a valuable reference for the sEMG-based control of bionic robot leg to generate human-like dynamic gait.

A novel biometric authentication approach using ECG and EMG signals.

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Belgacem, Noureddine; Fournier, Régis; Nait-Ali, Amine; Bereksi-Reguig, Fethi

2015-05-01

Security biometrics is a secure alternative to traditional methods of identity verification of individuals, such as authentication systems based on user name and password. Recently, it has been found that the electrocardiogram (ECG) signal formed by five successive waves (P, Q, R, S and T) is unique to each individual. In fact, better than any other biometrics' measures, it delivers proof of subject's being alive as extra information which other biometrics cannot deliver. The main purpose of this work is to present a low-cost method for online acquisition and processing of ECG signals for person authentication and to study the possibility of providing additional information and retrieve personal data from an electrocardiogram signal to yield a reliable decision. This study explores the effectiveness of a novel biometric system resulting from the fusion of information and knowledge provided by ECG and EMG (Electromyogram) physiological recordings. It is shown that biometrics based on these ECG/EMG signals offers a novel way to robustly authenticate subjects. Five ECG databases (MIT-BIH, ST-T, NSR, PTB and ECG-ID) and several ECG signals collected in-house from volunteers were exploited. A palm-based ECG biometric system was developed where the signals are collected from the palm of the subject through a minimally intrusive one-lead ECG set-up. A total of 3750 ECG beats were used in this work. Feature extraction was performed on ECG signals using Fourier descriptors (spectral coefficients). Optimum-Path Forest classifier was used to calculate the degree of similarity between individuals. The obtained results from the proposed approach look promising for individuals' authentication.

EMG-Torque Relation in Chronic Stroke: A Novel EMG Complexity Representation With a Linear Electrode Array.

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Zhang, Xu; Wang, Dongqing; Yu, Zaiyang; Chen, Xiang; Li, Sheng; Zhou, Ping

2017-11-01

This study examines the electromyogram (EMG)-torque relation for chronic stroke survivors using a novel EMG complexity representation. Ten stroke subjects performed a series of submaximal isometric elbow flexion tasks using their affected and contralateral arms, respectively, while a 20-channel linear electrode array was used to record surface EMG from the biceps brachii muscles. The sample entropy (SampEn) of surface EMG signals was calculated with both global and local tolerance schemes. A regression analysis was performed between SampEn of each channel's surface EMG and elbow flexion torque. It was found that a linear regression can be used to well describe the relation between surface EMG SampEn and the torque. Each channel's root mean square (RMS) amplitude of surface EMG signal in the different torque level was computed to determine the channel with the highest EMG amplitude. The slope of the regression (observed from the channel with the highest EMG amplitude) was smaller on the impaired side than on the nonimpaired side in 8 of the 10 subjects, regardless of the tolerance scheme (global or local) and the range of torques (full or matched range) used for comparison. The surface EMG signals from the channels above the estimated muscle innervation zones demonstrated significantly lower levels of complexity compared with other channels between innervation zones and muscle tendons. The study provides a novel point of view of the EMG-torque relation in the complexity domain, and reveals its alterations post stroke, which are associated with complex neural and muscular changes post stroke. The slope difference between channels with regard to innervation zones also confirms the relevance of electrode position in surface EMG analysis.

Comparison of sEMG-Based Feature Extraction and Motion Classification Methods for Upper-Limb Movement

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Shuxiang Guo

2015-04-01

Full Text Available The surface electromyography (sEMG technique is proposed for muscle activation detection and intuitive control of prostheses or robot arms. Motion recognition is widely used to map sEMG signals to the target motions. One of the main factors preventing the implementation of this kind of method for real-time applications is the unsatisfactory motion recognition rate and time consumption. The purpose of this paper is to compare eight combinations of four feature extraction methods (Root Mean Square (RMS, Detrended Fluctuation Analysis (DFA, Weight Peaks (WP, and Muscular Model (MM and two classifiers (Neural Networks (NN and Support Vector Machine (SVM, for the task of mapping sEMG signals to eight upper-limb motions, to find out the relation between these methods and propose a proper combination to solve this issue. Seven subjects participated in the experiment and six muscles of the upper-limb were selected to record sEMG signals. The experimental results showed that NN classifier obtained the highest recognition accuracy rate (88.7% during the training process while SVM performed better in real-time experiments (85.9%. For time consumption, SVM took less time than NN during the training process but needed more time for real-time computation. Among the four feature extraction methods, WP had the highest recognition rate for the training process (97.7% while MM performed the best during real-time tests (94.3%. The combination of MM and NN is recommended for strict real-time applications while a combination of MM and SVM will be more suitable when time consumption is not a key requirement.

FEATURE EXTRACTION FOR EMG BASED PROSTHESES CONTROL

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R. Aishwarya

2013-01-01

Full Text Available The control of prosthetic limb would be more effective if it is based on Surface Electromyogram (SEMG signals from remnant muscles. The analysis of SEMG signals depend on a number of factors, such as amplitude as well as time- and frequency-domain properties. Time series analysis using Auto Regressive (AR model and Mean frequency which is tolerant to white Gaussian noise are used as feature extraction techniques. EMG Histogram is used as another feature vector that was seen to give more distinct classification. The work was done with SEMG dataset obtained from the NINAPRO DATABASE, a resource for bio robotics community. Eight classes of hand movements hand open, hand close, Wrist extension, Wrist flexion, Pointing index, Ulnar deviation, Thumbs up, Thumb opposite to little finger are taken into consideration and feature vectors are extracted. The feature vectors can be given to an artificial neural network for further classification in controlling the prosthetic arm which is not dealt in this paper.

Stretchable human-machine interface based on skin-conformal sEMG electrodes with self-similar geometry

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Dong, Wentao; Zhu, Chen; Hu, Wei; Xiao, Lin; Huang, Yong'an

2018-01-01

Current stretchable surface electrodes have attracted increasing attention owing to their potential applications in biological signal monitoring, wearable human-machine interfaces (HMIs) and the Internet of Things. The paper proposed a stretchable HMI based on a surface electromyography (sEMG) electrode with a self-similar serpentine configuration. The sEMG electrode was transfer-printed onto the skin surface conformally to monitor biological signals, followed by signal classification and controlling of a mobile robot. Such electrodes can bear rather large deformation (such as >30%) under an appropriate areal coverage. The sEMG electrodes have been used to record electrophysiological signals from different parts of the body with sharp curvature, such as the index finger, back of the neck and face, and they exhibit great potential for HMI in the fields of robotics and healthcare. The electrodes placed onto the two wrists would generate two different signals with the fist clenched and loosened. It is classified to four kinds of signals with a combination of the gestures from the two wrists, that is, four control modes. Experiments demonstrated that the electrodes were successfully used as an HMI to control the motion of a mobile robot remotely. Project supported by the National Natural Science Foundation of China (Nos. 51635007, 91323303).

An ICA-EBM-Based sEMG Classifier for Recognizing Lower Limb Movements in Individuals With and Without Knee Pathology.

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Naik, Ganesh R; Selvan, S Easter; Arjunan, Sridhar P; Acharyya, Amit; Kumar, Dinesh K; Ramanujam, Arvind; Nguyen, Hung T

2018-03-01

Surface electromyography (sEMG) data acquired during lower limb movements has the potential for investigating knee pathology. Nevertheless, a major challenge encountered with sEMG signals generated by lower limb movements is the intersubject variability, because the signals recorded from the leg or thigh muscles are contingent on the characteristics of a subject such as gait activity and muscle structure. In order to cope with this difficulty, we have designed a three-step classification scheme. First, the multichannel sEMG is decomposed into activities of the underlying sources by means of independent component analysis via entropy bound minimization. Next, a set of time-domain features, which would best discriminate various movements, are extracted from the source estimates. Finally, the feature selection is performed with the help of the Fisher score and a scree-plot-based statistical technique, prior to feeding the dimension-reduced features to the linear discriminant analysis. The investigation involves 11 healthy subjects and 11 individuals with knee pathology performing three different lower limb movements, namely, walking, sitting, and standing, which yielded an average classification accuracy of 96.1% and 86.2%, respectively. While the outcome of this study per se is very encouraging, with suitable improvement, the clinical application of such an sEMG-based pattern recognition system that distinguishes healthy and knee pathological subjects would be an attractive consequence.

Development of Hand Grip Assistive Device Control System for Old People through Electromyography (EMG Signal Acquisitions

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Khamis Herman

2017-01-01

Full Text Available The hand grip assistive device is a glove to assist old people who suffer from hand weakness in their daily life activities. The device earlier control system only use simple on and off switch. This required old people to use both hand to activate the device. The new control system of the hand grip assistive device was developed to allow single hand operation for old people. New control system take advantages of electromyography (EMG and flex sensor which was implemented to the device. It was programmed into active and semi-active mode operation. EMG sensors were placed on the forearm to capture EMG signal of Flexor Digitorum Profundus muscle to activate the device. Flex sensor was used to indicate the finger position and placed on top of the finger. The signal from both sensors then used to control the device. The new control system allowed single hand operation and designed to prevent user from over depended on the device by activating it through moving their fingers.

A Versatile Embedded Platform for EMG Acquisition and Gesture Recognition.

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Benatti, Simone; Casamassima, Filippo; Milosevic, Bojan; Farella, Elisabetta; Schönle, Philipp; Fateh, Schekeb; Burger, Thomas; Huang, Qiuting; Benini, Luca

2015-10-01

Wearable devices offer interesting features, such as low cost and user friendliness, but their use for medical applications is an open research topic, given the limited hardware resources they provide. In this paper, we present an embedded solution for real-time EMG-based hand gesture recognition. The work focuses on the multi-level design of the system, integrating the hardware and software components to develop a wearable device capable of acquiring and processing EMG signals for real-time gesture recognition. The system combines the accuracy of a custom analog front end with the flexibility of a low power and high performance microcontroller for on-board processing. Our system achieves the same accuracy of high-end and more expensive active EMG sensors used in applications with strict requirements on signal quality. At the same time, due to its flexible configuration, it can be compared to the few wearable platforms designed for EMG gesture recognition available on market. We demonstrate that we reach similar or better performance while embedding the gesture recognition on board, with the benefit of cost reduction. To validate this approach, we collected a dataset of 7 gestures from 4 users, which were used to evaluate the impact of the number of EMG channels, the number of recognized gestures and the data rate on the recognition accuracy and on the computational demand of the classifier. As a result, we implemented a SVM recognition algorithm capable of real-time performance on the proposed wearable platform, achieving a classification rate of 90%, which is aligned with the state-of-the-art off-line results and a 29.7 mW power consumption, guaranteeing 44 hours of continuous operation with a 400 mAh battery.

Generating Control Commands From Gestures Sensed by EMG

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Wheeler, Kevin R.; Jorgensen, Charles

2006-01-01

An effort is under way to develop noninvasive neuro-electric interfaces through which human operators could control systems as diverse as simple mechanical devices, computers, aircraft, and even spacecraft. The basic idea is to use electrodes on the surface of the skin to acquire electromyographic (EMG) signals associated with gestures, digitize and process the EMG signals to recognize the gestures, and generate digital commands to perform the actions signified by the gestures. In an experimental prototype of such an interface, the EMG signals associated with hand gestures are acquired by use of several pairs of electrodes mounted in sleeves on a subject s forearm (see figure). The EMG signals are sampled and digitized. The resulting time-series data are fed as input to pattern-recognition software that has been trained to distinguish gestures from a given gesture set. The software implements, among other things, hidden Markov models, which are used to recognize the gestures as they are being performed in real time. Thus far, two experiments have been performed on the prototype interface to demonstrate feasibility: an experiment in synthesizing the output of a joystick and an experiment in synthesizing the output of a computer or typewriter keyboard. In the joystick experiment, the EMG signals were processed into joystick commands for a realistic flight simulator for an airplane. The acting pilot reached out into the air, grabbed an imaginary joystick, and pretended to manipulate the joystick to achieve left and right banks and up and down pitches of the simulated airplane. In the keyboard experiment, the subject pretended to type on a numerical keypad, and the EMG signals were processed into keystrokes. The results of the experiments demonstrate the basic feasibility of this method while indicating the need for further research to reduce the incidence of errors (including confusion among gestures). Topics that must be addressed include the numbers and arrangements

Continuous and simultaneous estimation of finger kinematics using inputs from an EMG-to-muscle activation model.

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Ngeo, Jimson G; Tamei, Tomoya; Shibata, Tomohiro

2014-08-14

Surface electromyography (EMG) signals are often used in many robot and rehabilitation applications because these reflect motor intentions of users very well. However, very few studies have focused on the accurate and proportional control of the human hand using EMG signals. Many have focused on discrete gesture classification and some have encountered inherent problems such as electro-mechanical delays (EMD). Here, we present a new method for estimating simultaneous and multiple finger kinematics from multi-channel surface EMG signals. In this study, surface EMG signals from the forearm and finger kinematic data were extracted from ten able-bodied subjects while they were tasked to do individual and simultaneous multiple finger flexion and extension movements in free space. Instead of using traditional time-domain features of EMG, an EMG-to-Muscle Activation model that parameterizes EMD was used and shown to give better estimation performance. A fast feed forward artificial neural network (ANN) and a nonparametric Gaussian Process (GP) regressor were both used and evaluated to estimate complex finger kinematics, with the latter rarely used in the other related literature. The estimation accuracies, in terms of mean correlation coefficient, were 0.85 ± 0.07, 0.78 ± 0.06 and 0.73 ± 0.04 for the metacarpophalangeal (MCP), proximal interphalangeal (PIP) and the distal interphalangeal (DIP) finger joint DOFs, respectively. The mean root-mean-square error in each individual DOF ranged from 5 to 15%. We show that estimation improved using the proposed muscle activation inputs compared to other features, and that using GP regression gave better estimation results when using fewer training samples. The proposed method provides a viable means of capturing the general trend of finger movements and shows a good way of estimating finger joint kinematics using a muscle activation model that parameterizes EMD. The results from this study demonstrates a potential control

Analyzing surface EMG signals to determine relationship between jaw imbalance and arm strength loss

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Truong Quang Dang Khoa

2012-08-01

Full Text Available Abstract Background This study investigated the relationship between dental occlusion and arm strength; in particular, the imbalance in the jaw can cause loss in arm strength phenomenon. One of the goals of this study was to record the maximum forces that the subjects can resist against the pull-down force on their hands while biting a spacer of adjustable height on the right or left side of the jaw. Then EMG measurement was used to determine the EMG-Force relationship of the jaw, neck and arms muscles. This gave us useful insights on the arms strength loss due to the biomechanical effects of the imbalance in the jaw mechanism. Methods In this study to determine the effects of the imbalance in the jaw to the strength of the arms, we conducted experiments with a pool of 20 healthy subjects of both genders. The subjects were asked to resist a pull down force applied on the contralateral arm while biting on a firm spacer using one side of the jaw. Four different muscles – masseter muscles, deltoid muscles, bicep muscles and trapezoid muscles – were involved. Integrated EMG (iEMG and Higuchi fractal dimension (HFD were used to analyze the EMG signals. Results The results showed that (1 Imbalance in the jaw causes loss of arm strength contra-laterally; (2 The loss is approximately a linear function of the height of the spacers. Moreover, the iEMG showed the intensity of muscle activities decreased when the degrees of jaw imbalance increased (spacer thickness increased. In addition, the tendency of Higuchi fractal dimension decreased for all muscles. Conclusions This finding indicates that muscle fatigue and the decrease in muscle contraction level leads to the loss of arm strength.

Prediction of Above-elbow Motions in Amputees, based on Electromyographic(EMG Signals, Using Nonlinear Autoregressive Exogenous (NARX Model

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Ali Akbar Akbari

2014-08-01

Full Text Available Introduction In order to improve the quality of life of amputees, biomechatronic researchers and biomedical engineers have been trying to use a combination of various techniques to provide suitable rehabilitation systems. Diverse biomedical signals, acquired from a specialized organ or cell system, e.g., the nervous system, are the driving force for the whole system. Electromyography(EMG, as an experimental technique,is concerned with the development, recording, and analysis of myoelectric signals. EMG-based research is making progress in the development of simple, robust, user-friendly, and efficient interface devices for the amputees. Materials and Methods Prediction of muscular activity and motion patterns is a common, practical problem in prosthetic organs. Recurrent neural network (RNN models are not only applicable for the prediction of time series, but are also commonly used for the control of dynamical systems. The prediction can be assimilated to identification of a dynamic process. An architectural approach of RNN with embedded memory is Nonlinear Autoregressive Exogenous (NARX model, which seems to be suitable for dynamic system applications. Results Performance of NARX model is verified for several chaotic time series, which are applied as input for the neural network. The results showed that NARX has the potential to capture the model of nonlinear dynamic systems. The R-value and MSE are  and  , respectively. Conclusion  EMG signals of deltoid and pectoralis major muscles are the inputs of the NARX  network. It is possible to obtain EMG signals of muscles in other arm motions to predict the lost functions of the absent arm in above-elbow amputees, using NARX model.

FastICA peel-off for ECG interference removal from surface EMG.

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Chen, Maoqi; Zhang, Xu; Chen, Xiang; Zhu, Mingxing; Li, Guanglin; Zhou, Ping

2016-06-13

Multi-channel recording of surface electromyographyic (EMG) signals is very likely to be contaminated by electrocardiographic (ECG) interference, specifically when the surface electrode is placed on muscles close to the heart. A novel fast independent component analysis (FastICA) based peel-off method is presented to remove ECG interference contaminating multi-channel surface EMG signals. Although demonstrating spatial variability in waveform shape, the ECG interference in different channels shares the same firing instants. Utilizing the firing information estimated from FastICA, ECG interference can be separated from surface EMG by a "peel off" processing. The performance of the method was quantified with synthetic signals by combining a series of experimentally recorded "clean" surface EMG and "pure" ECG interference. It was demonstrated that the new method can remove ECG interference efficiently with little distortion to surface EMG amplitude and frequency. The proposed method was also validated using experimental surface EMG signals contaminated by ECG interference. The proposed FastICA peel-off method can be used as a new and practical solution to eliminating ECG interference from multichannel EMG recordings.

Robust Bio-Signal Based Control of an Intelligent Wheelchair

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Dongyi Chen

2013-09-01

Full Text Available In this paper, an adaptive human-machine interaction (HMI method that is based on surface electromyography (sEMG signals is proposed for the hands-free control of an intelligent wheelchair. sEMG signals generated by the facial movements are obtained by a convenient dry electrodes sensing device. After the signals features are extracted from the autoregressive model, control data samples are updated and trained by an incremental online learning algorithm in real-time. Experimental results show that the proposed method can significantly improve the classification accuracy and training speed. Moreover, this method can effectively reduce the influence of muscle fatigue during a long time operation of sEMG-based HMI.

A Study on EMG-based Biometrics

OpenAIRE

Jin Su Kim; Sung Bum Pan

2017-01-01

Biometrics is a technology that recognizes user's information by using unique physical features of his or her body such as face, fingerprint, and iris. It also uses behavioral features such as signature, electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG). Among them, the EMG signal is a sign generated when the muscles move, which can be used in various fields such as motion recognition, personal identification, and disease diagnosis. In this paper, we analyze EMG-ba...

Capacitively coupled EMG detection via ultra-low-power microcontroller STFT.

Science.gov (United States)

Roland, Theresa; Baumgartner, Werner; Amsuess, Sebastian; Russold, Michael F

2017-07-01

As motion artefacts are a major problem with electromyography sensors, a new algorithm is developed to differentiate artefacts to contraction EMG. The performance of myoelectric prosthesis is increased with this algorithm. The implementation is done for an ultra-low-power microcontroller with limited calculation resources and memory. Short Time Fourier Transformation is used to enable real-time application. The sum of the differences (SOD) of the currently measured EMG to a reference contraction EMG is calculated. The SOD is a new parameter introduced for EMG classification. The satisfactory error rates are determined by measurements done with the capacitively coupling EMG prototype, recently developed by the research group.

Subspace based adaptive denoising of surface EMG from neurological injury patients

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Liu, Jie; Ying, Dongwen; Zev Rymer, William; Zhou, Ping

2014-10-01

Objective: After neurological injuries such as spinal cord injury, voluntary surface electromyogram (EMG) signals recorded from affected muscles are often corrupted by interferences, such as spurious involuntary spikes and background noises produced by physiological and extrinsic/accidental origins, imposing difficulties for signal processing. Conventional methods did not well address the problem caused by interferences. It is difficult to mitigate such interferences using conventional methods. The aim of this study was to develop a subspace-based denoising method to suppress involuntary background spikes contaminating voluntary surface EMG recordings. Approach: The Karhunen-Loeve transform was utilized to decompose a noisy signal into a signal subspace and a noise subspace. An optimal estimate of EMG signal is derived from the signal subspace and the noise power. Specifically, this estimator is capable of making a tradeoff between interference reduction and signal distortion. Since the estimator partially relies on the estimate of noise power, an adaptive method was presented to sequentially track the variation of interference power. The proposed method was evaluated using both semi-synthetic and real surface EMG signals. Main results: The experiments confirmed that the proposed method can effectively suppress interferences while keep the distortion of voluntary EMG signal in a low level. The proposed method can greatly facilitate further signal processing, such as onset detection of voluntary muscle activity. Significance: The proposed method can provide a powerful tool for suppressing background spikes and noise contaminating voluntary surface EMG signals of paretic muscles after neurological injuries, which is of great importance for their multi-purpose applications.

Sleep staging with movement-related signals.

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Jansen, B H; Shankar, K

1993-05-01

Body movement related signals (i.e., activity due to postural changes and the ballistocardiac effort) were recorded from six normal volunteers using the static-charge-sensitive bed (SCSB). Visual sleep staging was performed on the basis of simultaneously recorded EEG, EMG and EOG signals. A statistical classification technique was used to determine if reliable sleep staging could be performed using only the SCSB signal. A classification rate of between 52% and 75% was obtained for sleep staging in the five conventional sleep stages and the awake state. These rates improved from 78% to 89% for classification between awake, REM and non-REM sleep and from 86% to 98% for awake versus asleep classification.

Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients.

Science.gov (United States)

Ebrahimi, Farideh; Mikaeili, Mohammad; Estrada, Edson; Nazeran, Homer

2008-01-01

Currently in the world there is an alarming number of people who suffer from sleep disorders. A number of biomedical signals, such as EEG, EMG, ECG and EOG are used in sleep labs among others for diagnosis and treatment of sleep related disorders. The usual method for sleep stage classification is visual inspection by a sleep specialist. This is a very time consuming and laborious exercise. Automatic sleep stage classification can facilitate this process. The definition of sleep stages and the sleep literature show that EEG signals are similar in Stage 1 of non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep. Therefore, in this work an attempt was made to classify four sleep stages consisting of Awake, Stage 1 + REM, Stage 2 and Slow Wave Stage based on the EEG signal alone. Wavelet packet coefficients and artificial neural networks were deployed for this purpose. Seven all night recordings from Physionet database were used in the study. The results demonstrated that these four sleep stages could be automatically discriminated from each other with a specificity of 94.4 +/- 4.5%, a of sensitivity 84.2+3.9% and an accuracy of 93.0 +/- 4.0%.

Blind Signal Classification via Spare Coding

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2016-04-10

Blind Signal Classification via Sparse Coding Youngjune Gwon MIT Lincoln Laboratory gyj@ll.mit.edu Siamak Dastangoo MIT Lincoln Laboratory sia...achieve blind signal classification with no prior knowledge about signals (e.g., MCS, pulse shaping) in an arbitrary RF channel. Since modulated RF...classification method. Our results indicate that we can separate different classes of digitally modulated signals from blind sampling with 70.3% recall and 24.6

Classification of amyotrophic lateral sclerosis disease based on convolutional neural network and reinforcement sample learning algorithm.

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Sengur, Abdulkadir; Akbulut, Yaman; Guo, Yanhui; Bajaj, Varun

2017-12-01

Electromyogram (EMG) signals contain useful information of the neuromuscular diseases like amyotrophic lateral sclerosis (ALS). ALS is a well-known brain disease, which can progressively degenerate the motor neurons. In this paper, we propose a deep learning based method for efficient classification of ALS and normal EMG signals. Spectrogram, continuous wavelet transform (CWT), and smoothed pseudo Wigner-Ville distribution (SPWVD) have been employed for time-frequency (T-F) representation of EMG signals. A convolutional neural network is employed to classify these features. In it, Two convolution layers, two pooling layer, a fully connected layer and a lost function layer is considered in CNN architecture. The CNN architecture is trained with the reinforcement sample learning strategy. The efficiency of the proposed implementation is tested on publicly available EMG dataset. The dataset contains 89 ALS and 133 normal EMG signals with 24 kHz sampling frequency. Experimental results show 96.80% accuracy. The obtained results are also compared with other methods, which show the superiority of the proposed method.

The impact of shoulder abduction loading on EMG-based intention detection of hand opening and closing after stroke.

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Lan, Yiyun; Yao, Jun; Dewald, Julius P A

2011-01-01

Many stroke patients are subject to limited hand functions in the paretic arm due to a significant loss of Corticospinal Tract (CST) fibers. A possible solution for this problem is to classify surface Electromyography (EMG) signals generated by hand movements and uses that to implement Functional Electrical Stimulation (FES). However, EMG usually presents an abnormal muscle coactivation pattern shown as increased coupling between muscles within and/or across joints after stroke. The resulting Abnormal Muscle Synergies (AMS) could make the classification more difficult in individuals with stroke, especially when attempting to use the hand together with other joints in the paretic arm. Therefore, this study is aimed at identifying the impact of AMS following stroke on EMG pattern recognition between two hand movements. In an effort to achieve this goal, 7 chronic hemiparetic chronic stroke subjects were recruited and asked to perform hand opening and closing movements at their paretic arm while being either fully supported by a virtual table or loaded with 25% of subject's maximum shoulder abduction force. During the execution of motor tasks EMG signals from the wrist flexors and extensors were simultaneously acquired. Our results showed that increased synergy-induced activity at elbow flexors, induced by increasing shoulder abduction loading, deteriorated the performance of EMG pattern recognition for hand opening for those with a weak grasp strength and EMG activity. However, no such impact on hand closing has yet been observed possibly because finger/wrist flexion is facilitated by the shoulder abduction-induced flexion synergy.

A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series.

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Chambon, Stanislas; Galtier, Mathieu N; Arnal, Pierrick J; Wainrib, Gilles; Gramfort, Alexandre

2018-04-01

Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of the signal of a sleep stage, based on the visual inspection of signals such as electroencephalograms (EEGs), electrooculograms (EOGs), electrocardiograms, and electromyograms (EMGs). We introduce here the first deep learning approach for sleep stage classification that learns end-to-end without computing spectrograms or extracting handcrafted features, that exploits all multivariate and multimodal polysomnography (PSG) signals (EEG, EMG, and EOG), and that can exploit the temporal context of each 30-s window of data. For each modality, the first layer learns linear spatial filters that exploit the array of sensors to increase the signal-to-noise ratio, and the last layer feeds the learnt representation to a softmax classifier. Our model is compared to alternative automatic approaches based on convolutional networks or decisions trees. Results obtained on 61 publicly available PSG records with up to 20 EEG channels demonstrate that our network architecture yields the state-of-the-art performance. Our study reveals a number of insights on the spatiotemporal distribution of the signal of interest: a good tradeoff for optimal classification performance measured with balanced accuracy is to use 6 EEG with 2 EOG (left and right) and 3 EMG chin channels. Also exploiting 1 min of data before and after each data segment offers the strongest improvement when a limited number of channels are available. As sleep experts, our system exploits the multivariate and multimodal nature of PSG signals in order to deliver the state-of-the-art classification performance with a small computational cost.

Surface EMG decomposition based on K-means clustering and convolution kernel compensation.

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Ning, Yong; Zhu, Xiangjun; Zhu, Shanan; Zhang, Yingchun

2015-03-01

A new approach has been developed by combining the K-mean clustering (KMC) method and a modified convolution kernel compensation (CKC) method for multichannel surface electromyogram (EMG) decomposition. The KMC method was first utilized to cluster vectors of observations at different time instants and then estimate the initial innervation pulse train (IPT). The CKC method, modified with a novel multistep iterative process, was conducted to update the estimated IPT. The performance of the proposed K-means clustering-Modified CKC (KmCKC) approach was evaluated by reconstructing IPTs from both simulated and experimental surface EMG signals. The KmCKC approach successfully reconstructed all 10 IPTs from the simulated surface EMG signals with true positive rates (TPR) of over 90% with a low signal-to-noise ratio (SNR) of -10 dB. More than 10 motor units were also successfully extracted from the 64-channel experimental surface EMG signals of the first dorsal interosseous (FDI) muscles when a contraction force was held at 8 N by using the KmCKC approach. A "two-source" test was further conducted with 64-channel surface EMG signals. The high percentage of common MUs and common pulses (over 92% at all force levels) between the IPTs reconstructed from the two independent groups of surface EMG signals demonstrates the reliability and capability of the proposed KmCKC approach in multichannel surface EMG decomposition. Results from both simulated and experimental data are consistent and confirm that the proposed KmCKC approach can successfully reconstruct IPTs with high accuracy at different levels of contraction.

A Study on EMG-based Biometrics

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Jin Su Kim

2017-05-01

Full Text Available Biometrics is a technology that recognizes user's information by using unique physical features of his or her body such as face, fingerprint, and iris. It also uses behavioral features such as signature, electrocardiogram (ECG, electromyogram (EMG, and electroencephalogram (EEG. Among them, the EMG signal is a sign generated when the muscles move, which can be used in various fields such as motion recognition, personal identification, and disease diagnosis. In this paper, we analyze EMG-based biometrics and implement a motion recognition and personal identification system. The system extracted features using non-uniform filter bank and Waveform Length (WL, and reduces the dimension using Principal Component Analysis (PCA and Linear Discriminant Analysis (LDA. Afterward, it classified the features using Euclidean Distance (ED, Support Vector Machine (SVM and K Nearest Neighbors (KNN. As a result of the motion recognition experiment, 95% of acquired EMG data and 84.66% of UCI data were obtained and as a result of the personal recognition experiment, 85% of acquired EMG data and 88.66% of UCI data were obtained.

Surface EMG system for use in long-term vigorous activities

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de Luca, G.; Bergman, P.; de Luca, C.

The purpose of the project was to develop an advanced surface electromyographic (EMG) system that is portable, un-tethered, and able to detect high-fidelity EMG signals from multiple channels. The innovation was specifically designed to extend NASA's capability to perform neurological status monitoring for long-term, vigorous activities. These features are a necessary requirement of ground-based and in-flight studies planned for the International Space Station and human expeditions to Mars. The project consisted of developing 1) a portable EMG digital data logger using a handheld PC for acquiring the signal and storing the data from as many as 8 channels, and 2) an EMG electrode/skin interface to improve signal fidelity and skin adhesion in the presence of sweat and mechanical disturbances encountered during vigorous activities. The system, referred to as a MyoMonitor, was configured with a communication port for downloading the data from the data logger to the PC computer workstation. Software specifications were developed and implemented for programming of acquisition protocols, power management, and transferring data to the PC for processing and graphical display. The prototype MyoMonitor was implemented using a handheld PC that features a color LCD screen, enhanced keyboard, extended Lithium Ion battery and recharger, and 128 Mbytes of F ash Memory. The system was designed to be belt-worn,l thereby allowing its use under vigorous activities. The Monitor utilizes up to 8 differential surface EMG sensors. The prototype allowed greater than 2 hours of continuous 8-channel EMG data to be collected, or 17.2 hours of continuous single channel EMG data. Standardized tests in human subjects were conducted to develop the mechanical and electrical properties of the prototype electrode/interface system. Tests conducted during treadmill running and repetitive lifting demonstrated that the prototype interface significantly reduced the detrimental effects of sweat

Experimental Study of Real-Time Classification of 17 Voluntary Movements for Multi-Degree Myoelectric Prosthetic Hand

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Trongmun Jiralerspong

2017-11-01

Full Text Available The myoelectric prosthetic hand is a powerful tool developed to help people with upper limb loss restore the functions of a biological hand. Recognizing multiple hand motions from only a few electromyography (EMG sensors is one of the requirements for the development of prosthetic hands with high level of usability. This task is highly challenging because both classification rate and misclassification rate worsen with additional hand motions. This paper presents a signal processing technique that uses spectral features and an artificial neural network to classify 17 voluntary movements from EMG signals. The main highlight will be on the use of a small set of low-cost EMG sensor for classification of a reasonably large number of hand movements. The aim of this work is to extend the capabilities to recognize and produce multiple movements beyond what is currently feasible. This work will also show and discuss about how tailoring the number of hand motions for a specific task can help develop a more reliable prosthetic hand system. Online classification experiments have been conducted on seven male and five female participants to evaluate the validity of the proposed method. The proposed algorithm achieves an overall correct classification rate of up to 83%, thus, demonstrating the potential to classify 17 movements from 6 EMG sensors. Furthermore, classifying 9 motions using this method could achieve an accuracy of up to 92%. These results show that if the prosthetic hand is intended for a specific task, limiting the number of motions can significantly increase the performance and usability.

EMG based FES for post-stroke rehabilitation

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Piyus, Ceethal K.; Anjaly Cherian, V.; Nageswaran, Sharmila

2017-11-01

Annually, 15 million in world population experiences stroke. Nearly 9 million stroke survivors every year experience mild to severe disability. The loss of upper extremity function in stroke survivors still remains a major rehabilitation challenge. The proposed EMG Abstract—Annually, 15 million in world population experiences stroke. Nearly 9 million stroke survivors every year experience mild to severe disability. The loss of upper extremity function in stroke survivors still remains a major rehabilitation challenge. The proposed EMG based FES system can be used for effective upper limb motor re-education in post stroke upper limb rehabilitation. The governing feature of the designed system is its synchronous activation, in which the FES stimulation is dependent on the amplitude of the EMG signal acquired from the unaffected upper limb muscle of the hemiplegic patient. This proportionate operation eliminates the undesirable damage to the patient’s skin by generating stimulus in proportion to voluntary EMG signals. This feature overcomes the disadvantages of currently available manual motor re-education systems. This model can be used in home-based post stroke rehabilitation, to effectively improve the upper limb functions.

A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series

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Chambon, Stanislas; Galtier, Mathieu; Arnal, Pierrick; Wainrib, Gilles; Gramfort, Alexandre

2017-01-01

Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30s of signal a sleep stage, based on the visual inspection of signals such as electroencephalograms (EEG), electrooculograms (EOG), electrocardiograms (ECG) and electromyograms (EMG). We introduce here the first deep learning approach for sleep stage classification that learns end-to-end without computing spectrograms or...

Effect of a pelvic belt on EMG activity during manual load lifting

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Marcelo Pinto Pereira

2009-04-01

Full Text Available Manual lifting (ML capacity is still a matter of concern for industry administrators and electromyography (EMG seems to be a good alternative for the evaluation of muscles involved in this task. However, the reliability of these measures is very important. Thus, the objective of this study was to evaluate the influence of a pelvic belt on EMG activity of the erector spinus (ES and rectus femoralis (RF muscles during ML and during maximal voluntary contractions (MVC of trunk extension performed before (baseline and after ML. In addition, the variabilityin the EMG signal normalized by the following three different methods was evaluated: peak EMG activity, mean EMG activity, and EMG activity obtained during MVC. Eight volunteers performed ML of 15% and 25% of their body weight for 1 minute in the presence or absence of a pelvic belt. The coefficient of variation (CV of the EMG signal obtained for the ES and RF muscles was calculated during ML. Load cell traction values and the electromyographic variables RMS, median frequency, mean power frequency and total power of the ES muscle were obtained during MVC. The results showed lower CV (smaller variability when the EMG signal was normalized by peak activity, with this method thus being preferable. During MVC, only the load cell traction value differed from baseline after ML of 25% body weight without the pelvic belt (p=0.035, a finding suggesting rapid recovery of ES muscle after ML for 1 minute.

Automatic Genre Classification of Musical Signals

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Barbedo, Jayme Garcia sArnal; Lopes, Amauri

2006-12-01

We present a strategy to perform automatic genre classification of musical signals. The technique divides the signals into 21.3 milliseconds frames, from which 4 features are extracted. The values of each feature are treated over 1-second analysis segments. Some statistical results of the features along each analysis segment are used to determine a vector of summary features that characterizes the respective segment. Next, a classification procedure uses those vectors to differentiate between genres. The classification procedure has two main characteristics: (1) a very wide and deep taxonomy, which allows a very meticulous comparison between different genres, and (2) a wide pairwise comparison of genres, which allows emphasizing the differences between each pair of genres. The procedure points out the genre that best fits the characteristics of each segment. The final classification of the signal is given by the genre that appears more times along all signal segments. The approach has shown very good accuracy even for the lowest layers of the hierarchical structure.

Compensatory neurofuzzy model for discrete data classification in biomedical

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Ceylan, Rahime

2015-03-01

Biomedical data is separated to two main sections: signals and discrete data. So, studies in this area are about biomedical signal classification or biomedical discrete data classification. There are artificial intelligence models which are relevant to classification of ECG, EMG or EEG signals. In same way, in literature, many models exist for classification of discrete data taken as value of samples which can be results of blood analysis or biopsy in medical process. Each algorithm could not achieve high accuracy rate on classification of signal and discrete data. In this study, compensatory neurofuzzy network model is presented for classification of discrete data in biomedical pattern recognition area. The compensatory neurofuzzy network has a hybrid and binary classifier. In this system, the parameters of fuzzy systems are updated by backpropagation algorithm. The realized classifier model is conducted to two benchmark datasets (Wisconsin Breast Cancer dataset and Pima Indian Diabetes dataset). Experimental studies show that compensatory neurofuzzy network model achieved 96.11% accuracy rate in classification of breast cancer dataset and 69.08% accuracy rate was obtained in experiments made on diabetes dataset with only 10 iterations.

Repeatability of grasp recognition for robotic hand prosthesis control based on sEMG data.

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Palermo, Francesca; Cognolato, Matteo; Gijsberts, Arjan; Muller, Henning; Caputo, Barbara; Atzori, Manfredo

2017-07-01

Control methods based on sEMG obtained promising results for hand prosthetics. Control system robustness is still often inadequate and does not allow the amputees to perform a large number of movements useful for everyday life. Only few studies analyzed the repeatability of sEMG classification of hand grasps. The main goals of this paper are to explore repeatability in sEMG data and to release a repeatability database with the recorded experiments. The data are recorded from 10 intact subjects repeating 7 grasps 12 times, twice a day for 5 days. The data are publicly available on the Ninapro web page. The analysis for the repeatability is based on the comparison of movement classification accuracy in several data acquisitions and for different subjects. The analysis is performed using mean absolute value and waveform length features and a Random Forest classifier. The accuracy obtained by training and testing on acquisitions at different times is on average 27.03% lower than training and testing on the same acquisition. The results obtained by training and testing on different acquisitions suggest that previous acquisitions can be used to train the classification algorithms. The inter-subject variability is remarkable, suggesting that specific characteristics of the subjects can affect repeatibility and sEMG classification accuracy. In conclusion, the results of this paper can contribute to develop more robust control systems for hand prostheses, while the presented data allows researchers to test repeatability in further analyses.

High-density surface EMG maps from upper-arm and forearm muscles

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Rojas-Martínez Monica

2012-12-01

Full Text Available Abstract Background sEMG signal has been widely used in different applications in kinesiology and rehabilitation as well as in the control of human-machine interfaces. In general, the signals are recorded with bipolar electrodes located in different muscles. However, such configuration may disregard some aspects of the spatial distribution of the potentials like location of innervation zones and the manifestation of inhomogineties in the control of the muscular fibers. On the other hand, the spatial distribution of motor unit action potentials has recently been assessed with activation maps obtained from High Density EMG signals (HD-EMG, these lasts recorded with arrays of closely spaced electrodes. The main objective of this work is to analyze patterns in the activation maps, associating them with four movement directions at the elbow joint and with different strengths of those tasks. Although the activation pattern can be assessed with bipolar electrodes, HD-EMG maps could enable the extraction of features that depend on the spatial distribution of the potentials and on the load-sharing between muscles, in order to have a better differentiation between tasks and effort levels. Methods An experimental protocol consisting of isometric contractions at three levels of effort during flexion, extension, supination and pronation at the elbow joint was designed and HD-EMG signals were recorded with 2D electrode arrays on different upper-limb muscles. Techniques for the identification and interpolation of artifacts are explained, as well as a method for the segmentation of the activation areas. In addition, variables related to the intensity and spatial distribution of the maps were obtained, as well as variables associated to signal power of traditional single bipolar recordings. Finally, statistical tests were applied in order to assess differences between information extracted from single bipolar signals or from HD-EMG maps and to analyze

Innervation zone of the vastus medialis muscle: position and effect on surface EMG variables

International Nuclear Information System (INIS)

Gallina, A; Merletti, R; Gazzoni, M

2013-01-01

The aim of this study was to investigate the position of the innervation zone (IZ) of the vastus medialis (VM) and its effect on the electromyographic (EMG) amplitude and mean frequency estimates. Eighteen healthy subjects performed maximal isometric knee extensions at three knee angles. Surface EMG signals were collected by using a 16 × 8 electrode grid placed on the VM muscle. The position of the IZ was estimated through visual analysis, and traditional bipolar signals were obtained from channels over and away from it; amplitude and mean frequency values were extracted and compared using an analysis of variance (ANOVA) with repeated measures. The IZ is shaped as a line running from the proximal–lateral to the distal–medial aspect of the VM muscle. The presence of an IZ under the electrodes lowered the EMG amplitude (P < 0.001, F = 58.11) and increased the EMG mean frequency (P < 0.001, F = 26.47); variations of these parameters due to the knee flexion angle were less frequently observed in EMG signals collected over than away from the IZ. Electrodes placed ‘over the belly of the VM muscle’ are likely to collect EMG signals influenced by the presence of the IZ, thus hindering the detection of changes in muscle activity. (paper)

Influence on muscle oxygenation to EMG parameters at different skeletal muscle contraction

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Zhang, Li; Song, Gaoqing

2010-02-01

The purpose of this study is to investigate the influence of muscle oxygenation on EMG parameters during isometric and incremental exercises and to observe the relationship between EMG parameters and muscle oxygenation. Twelve rowers took part in the tests. Near infrared spectrometer was utilized for measurements of muscle oxygenation on lateral quadriceps. sEMG measurement is performed for EMG parameters during isometric and incremental exercises. Results indicated that Oxy-Hb decrease significantly correlated with IEMG, E/T ratio and frequency of impulse signal during 1/3 MVC and 2/3 MVC isometric exercise, and it is also correlated with IEMG, E/T ratio and frequency of impulse signal. Increase of IEMG occurred at the time after Oxy-Hb decrease during incremental exercise and highly correlated with BLa. It is concluded that no matter how heavy the intensity is, Oxy-Hb dissociation may play an important role in affecting EMG parameters of muscle fatigue during isometric exercise. 2) EMG parameters may be influenced by Oxy-Hb dissociation and blood lactate concentration during dynamic exercise.

Classification of EMG signals using artificial neural networks for virtual hand prosthesis control.

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Mattioli, Fernando E R; Lamounier, Edgard A; Cardoso, Alexandre; Soares, Alcimar B; Andrade, Adriano O

2011-01-01

Computer-based training systems have been widely studied in the field of human rehabilitation. In health applications, Virtual Reality presents itself as an appropriate tool to simulate training environments without exposing the patients to risks. In particular, virtual prosthetic devices have been used to reduce the great mental effort needed by patients fitted with myoelectric prosthesis, during the training stage. In this paper, the application of Virtual Reality in a hand prosthesis training system is presented. To achieve this, the possibility of exploring Neural Networks in a real-time classification system is discussed. The classification technique used in this work resulted in a 95% success rate when discriminating 4 different hand movements.

A sEMG model with experimentally based simulation parameters.

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Wheeler, Katherine A; Shimada, Hiroshima; Kumar, Dinesh K; Arjunan, Sridhar P

2010-01-01

A differential, time-invariant, surface electromyogram (sEMG) model has been implemented. While it is based on existing EMG models, the novelty of this implementation is that it assigns more accurate distributions of variables to create realistic motor unit (MU) characteristics. Variables such as muscle fibre conduction velocity, jitter (the change in the interpulse interval between subsequent action potential firings) and motor unit size have been considered to follow normal distributions about an experimentally obtained mean. In addition, motor unit firing frequencies have been considered to have non-linear and type based distributions that are in accordance with experimental results. Motor unit recruitment thresholds have been considered to be related to the MU type. The model has been used to simulate single channel differential sEMG signals from voluntary, isometric contractions of the biceps brachii muscle. The model has been experimentally verified by conducting experiments on three subjects. Comparison between simulated signals and experimental recordings shows that the Root Mean Square (RMS) increases linearly with force in both cases. The simulated signals also show similar values and rates of change of RMS to the experimental signals.

Latent Factors Limiting the Performance of sEMG-Interfaces

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Sergey Lobov

2018-04-01

Full Text Available Recent advances in recording and real-time analysis of surface electromyographic signals (sEMG have fostered the use of sEMG human–machine interfaces for controlling personal computers, prostheses of upper limbs, and exoskeletons among others. Despite a relatively high mean performance, sEMG-interfaces still exhibit strong variance in the fidelity of gesture recognition among different users. Here, we systematically study the latent factors determining the performance of sEMG-interfaces in synthetic tests and in an arcade game. We show that the degree of muscle cooperation and the amount of the body fatty tissue are the decisive factors in synthetic tests. Our data suggest that these factors can only be adjusted by long-term training, which promotes fine-tuning of low-level neural circuits driving the muscles. Short-term training has no effect on synthetic tests, but significantly increases the game scoring. This implies that it works at a higher decision-making level, not relevant for synthetic gestures. We propose a procedure that enables quantification of the gestures’ fidelity in a dynamic gaming environment. For each individual subject, the approach allows identifying “problematic” gestures that decrease gaming performance. This information can be used for optimizing the training strategy and for adapting the signal processing algorithms to individual users, which could be a way for a qualitative leap in the development of future sEMG-interfaces.

Signal classification for acoustic neutrino detection

International Nuclear Information System (INIS)

Neff, M.; Anton, G.; Enzenhöfer, A.; Graf, K.; Hößl, J.; Katz, U.; Lahmann, R.; Richardt, C.

2012-01-01

This article focuses on signal classification for deep-sea acoustic neutrino detection. In the deep sea, the background of transient signals is very diverse. Approaches like matched filtering are not sufficient to distinguish between neutrino-like signals and other transient signals with similar signature, which are forming the acoustic background for neutrino detection in the deep-sea environment. A classification system based on machine learning algorithms is analysed with the goal to find a robust and effective way to perform this task. For a well-trained model, a testing error on the level of 1% is achieved for strong classifiers like Random Forest and Boosting Trees using the extracted features of the signal as input and utilising dense clusters of sensors instead of single sensors.

EMG-Torque Dynamics Change With Contraction Bandwidth.

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Golkar, Mahsa A; Jalaleddini, Kian; Kearney, Robert E

2018-04-01

An accurate model for ElectroMyoGram (EMG)-torque dynamics has many uses. One of its applications which has gained high attention among researchers is its use, in estimating the muscle contraction level for the efficient control of prosthesis. In this paper, the dynamic relationship between the surface EMG and torque during isometric contractions at the human ankle was studied using system identification techniques. Subjects voluntarily modulated their ankle torque in dorsiflexion direction, by activating their tibialis anterior muscle, while tracking a pseudo-random binary sequence in a torque matching task. The effects of contraction bandwidth, described by torque spectrum, on EMG-torque dynamics were evaluated by varying the visual command switching time. Nonparametric impulse response functions (IRF) were estimated between the processed surface EMG and torque. It was demonstrated that: 1) at low contraction bandwidths, the identified IRFs had unphysiological anticipatory (i.e., non-causal) components, whose amplitude decreased as the contraction bandwidth increased. We hypothesized that this non-causal behavior arose, because the EMG input contained a component due to feedback from the output torque, i.e., it was recorded from within a closed-loop. Vision was not the feedback source since the non-causal behavior persisted when visual feedback was removed. Repeating the identification using a nonparametric closed-loop identification algorithm yielded causal IRFs at all bandwidths, supporting this hypothesis. 2) EMG-torque dynamics became faster and the bandwidth of system increased as contraction modulation rate increased. Thus, accurate prediction of torque from EMG signals must take into account the contraction bandwidth sensitivity of this system.

A new algorithm for ECG interference removal from single channel EMG recording.

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Yazdani, Shayan; Azghani, Mahmood Reza; Sedaaghi, Mohammad Hossein

2017-09-01

This paper presents a new method to remove electrocardiogram (ECG) interference from electromyogram (EMG). This interference occurs during the EMG acquisition from trunk muscles. The proposed algorithm employs progressive image denoising (PID) algorithm and ensembles empirical mode decomposition (EEMD) to remove this type of interference. PID is a very recent method that is being used for denoising digital images mixed with white Gaussian noise. It detects white Gaussian noise by deterministic annealing. To the best of our knowledge, PID has never been used before, in the case of EMG and ECG separation or in other 1D signal denoising applications. We have used it according to this fact that amplitude of the EMG signal can be modeled as white Gaussian noise using a filter with time-variant properties. The proposed algorithm has been compared to the other well-known methods such as HPF, EEMD-ICA, Wavelet-ICA and PID. The results show that the proposed algorithm outperforms the others, on the basis of three evaluation criteria used in this paper: Normalized mean square error, Signal to noise ratio and Pearson correlation.

Evaluation of EMG processing techniques using Information Theory.

Science.gov (United States)

Farfán, Fernando D; Politti, Julio C; Felice, Carmelo J

2010-11-12

Electromyographic signals can be used in biomedical engineering and/or rehabilitation field, as potential sources of control for prosthetics and orthotics. In such applications, digital processing techniques are necessary to follow efficient and effectively the changes in the physiological characteristics produced by a muscular contraction. In this paper, two methods based on information theory are proposed to evaluate the processing techniques. These methods determine the amount of information that a processing technique is able to extract from EMG signals. The processing techniques evaluated with these methods were: absolute mean value (AMV), RMS values, variance values (VAR) and difference absolute mean value (DAMV). EMG signals from the middle deltoid during abduction and adduction movement of the arm in the scapular plane was registered, for static and dynamic contractions. The optimal window length (segmentation), abduction and adduction movements and inter-electrode distance were also analyzed. Using the optimal segmentation (200 ms and 300 ms in static and dynamic contractions, respectively) the best processing techniques were: RMS, AMV and VAR in static contractions, and only the RMS in dynamic contractions. Using the RMS of EMG signal, variations in the amount of information between the abduction and adduction movements were observed. Although the evaluation methods proposed here were applied to standard processing techniques, these methods can also be considered as alternatives tools to evaluate new processing techniques in different areas of electrophysiology.

Neural network committees for finger joint angle estimation from surface EMG signals

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Reddy Narender P

2009-01-01

Full Text Available Abstract Background In virtual reality (VR systems, the user's finger and hand positions are sensed and used to control the virtual environments. Direct biocontrol of VR environments using surface electromyography (SEMG signals may be more synergistic and unconstraining to the user. The purpose of the present investigation was to develop a technique to predict the finger joint angle from the surface EMG measurements of the extensor muscle using neural network models. Methodology SEMG together with the actual joint angle measurements were obtained while the subject was performing flexion-extension rotation of the index finger at three speeds. Several neural networks were trained to predict the joint angle from the parameters extracted from the SEMG signals. The best networks were selected to form six committees. The neural network committees were evaluated using data from new subjects. Results There was hysteresis in the measured SMEG signals during the flexion-extension cycle. However, neural network committees were able to predict the joint angle with reasonable accuracy. RMS errors ranged from 0.085 ± 0.036 for fast speed finger-extension to 0.147 ± 0.026 for slow speed finger extension, and from 0.098 ± 0.023 for the fast speed finger flexion to 0.163 ± 0.054 for slow speed finger flexion. Conclusion Although hysteresis was observed in the measured SEMG signals, the committees of neural networks were able to predict the finger joint angle from SEMG signals.

Physiological modules for generating discrete and rhythmic movements: component analysis of EMG signals.

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Bengoetxea, Ana; Leurs, Françoise; Hoellinger, Thomas; Cebolla, Ana Maria; Dan, Bernard; Cheron, Guy; McIntyre, Joseph

2014-01-01

A central question in Neuroscience is that of how the nervous system generates the spatiotemporal commands needed to realize complex gestures, such as handwriting. A key postulate is that the central nervous system (CNS) builds up complex movements from a set of simpler motor primitives or control modules. In this study we examined the control modules underlying the generation of muscle activations when performing different types of movement: discrete, point-to-point movements in eight different directions and continuous figure-eight movements in both the normal, upright orientation and rotated 90°. To test for the effects of biomechanical constraints, movements were performed in the frontal-parallel or sagittal planes, corresponding to two different nominal flexion/abduction postures of the shoulder. In all cases we measured limb kinematics and surface electromyographic activity (EMG) signals for seven different muscles acting around the shoulder. We first performed principal component analysis (PCA) of the EMG signals on a movement-by-movement basis. We found a surprisingly consistent pattern of muscle groupings across movement types and movement planes, although we could detect systematic differences between the PCs derived from movements performed in each shoulder posture and between the principal components associated with the different orientations of the figure. Unexpectedly we found no systematic differences between the figure eights and the point-to-point movements. The first three principal components could be associated with a general co-contraction of all seven muscles plus two patterns of reciprocal activation. From these results, we surmise that both "discrete-rhythmic movements" such as the figure eight, and discrete point-to-point movement may be constructed from three different fundamental modules, one regulating the impedance of the limb over the time span of the movement and two others operating to generate movement, one aligned with the

A hybrid BMI-based exoskeleton for paresis: EMG control for assisting arm movements.

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Kawase, Toshihiro; Sakurada, Takeshi; Koike, Yasuharu; Kansaku, Kenji

2017-02-01

Brain-machine interface (BMI) technologies have succeeded in controlling robotic exoskeletons, enabling some paralyzed people to control their own arms and hands. We have developed an exoskeleton asynchronously controlled by EEG signals. In this study, to enable real-time control of the exoskeleton for paresis, we developed a hybrid system with EEG and EMG signals, and the EMG signals were used to estimate its joint angles. Eleven able-bodied subjects and two patients with upper cervical spinal cord injuries (SCIs) performed hand and arm movements, and the angles of the metacarpophalangeal (MP) joint of the index finger, wrist, and elbow were estimated from EMG signals using a formula that we derived to calculate joint angles from EMG signals, based on a musculoskeletal model. The formula was exploited to control the elbow of the exoskeleton after automatic adjustments. Four able-bodied subjects and a patient with upper cervical SCI wore an exoskeleton controlled using EMG signals and were required to perform hand and arm movements to carry and release a ball. Estimated angles of the MP joints of index fingers, wrists, and elbows were correlated well with the measured angles in 11 able-bodied subjects (correlation coefficients were 0.81  ±  0.09, 0.85  ±  0.09, and 0.76  ±  0.13, respectively) and the patients (e.g. 0.91  ±  0.01 in the elbow of a patient). Four able-bodied subjects successfully positioned their arms to adequate angles by extending their elbows and a joint of the exoskeleton, with root-mean-square errors  exoskeleton, successfully carried a ball to a goal in all 10 trials. A BMI-based exoskeleton for paralyzed arms and hands using real-time control was realized by designing a new method to estimate joint angles based on EMG signals, and these may be useful for practical rehabilitation and the support of daily actions.

A hybrid BMI-based exoskeleton for paresis: EMG control for assisting arm movements

Science.gov (United States)

Kawase, Toshihiro; Sakurada, Takeshi; Koike, Yasuharu; Kansaku, Kenji

2017-02-01

Objective. Brain-machine interface (BMI) technologies have succeeded in controlling robotic exoskeletons, enabling some paralyzed people to control their own arms and hands. We have developed an exoskeleton asynchronously controlled by EEG signals. In this study, to enable real-time control of the exoskeleton for paresis, we developed a hybrid system with EEG and EMG signals, and the EMG signals were used to estimate its joint angles. Approach. Eleven able-bodied subjects and two patients with upper cervical spinal cord injuries (SCIs) performed hand and arm movements, and the angles of the metacarpophalangeal (MP) joint of the index finger, wrist, and elbow were estimated from EMG signals using a formula that we derived to calculate joint angles from EMG signals, based on a musculoskeletal model. The formula was exploited to control the elbow of the exoskeleton after automatic adjustments. Four able-bodied subjects and a patient with upper cervical SCI wore an exoskeleton controlled using EMG signals and were required to perform hand and arm movements to carry and release a ball. Main results. Estimated angles of the MP joints of index fingers, wrists, and elbows were correlated well with the measured angles in 11 able-bodied subjects (correlation coefficients were 0.81  ±  0.09, 0.85  ±  0.09, and 0.76  ±  0.13, respectively) and the patients (e.g. 0.91  ±  0.01 in the elbow of a patient). Four able-bodied subjects successfully positioned their arms to adequate angles by extending their elbows and a joint of the exoskeleton, with root-mean-square errors  exoskeleton, successfully carried a ball to a goal in all 10 trials. Significance. A BMI-based exoskeleton for paralyzed arms and hands using real-time control was realized by designing a new method to estimate joint angles based on EMG signals, and these may be useful for practical rehabilitation and the support of daily actions.

Associations between motor unit action potential parameters and surface EMG features.

Science.gov (United States)

Del Vecchio, Alessandro; Negro, Francesco; Felici, Francesco; Farina, Dario

2017-10-01

The surface interference EMG signal provides some information on the neural drive to muscles. However, the association between neural drive to muscle and muscle activation has long been debated with controversial indications due to the unavailability of motor unit population data. In this study, we clarify the potential and limitations of interference EMG analysis to infer motor unit recruitment strategies with an experimental investigation of several concurrently active motor units and of the associated features of the surface EMG. For this purpose, we recorded high-density surface EMG signals during linearly increasing force contractions of the tibialis anterior muscle, up to 70% of maximal force. The recruitment threshold (RT), conduction velocity (MUCV), median frequency (MDF MU ), and amplitude (RMS MU ) of action potentials of 587 motor units from 13 individuals were assessed and associated with features of the interference EMG. MUCV was positively associated with RT ( R 2 = 0.64 ± 0.14), whereas MDF MU and RMS MU showed a weaker relation with RT ( R 2 = 0.11 ± 0.11 and 0.39 ± 0.24, respectively). Moreover, the changes in average conduction velocity estimated from the interference EMG predicted well the changes in MUCV ( R 2 = 0.71), with a strong association to ankle dorsiflexion force ( R 2 = 0.81 ± 0.12). Conversely, both the average EMG MDF and RMS were poorly associated with motor unit recruitment. These results clarify the limitations of EMG spectral and amplitude analysis in inferring the neural strategies of muscle control and indicate that, conversely, the average conduction velocity could provide relevant information on these strategies. NEW & NOTEWORTHY The surface EMG provides information on the neural drive to muscles. However, the associations between EMG features and neural drive have been long debated due to unavailability of motor unit population data. Here, by using novel highly accurate decomposition of the EMG, we related motor unit

Real-time muscle state estimation from EMG signals during isometric contractions using Kalman filters.

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Menegaldo, Luciano L

2017-12-01

State-space control of myoelectric devices and real-time visualization of muscle forces in virtual rehabilitation require measuring or estimating muscle dynamic states: neuromuscular activation, tendon force and muscle length. This paper investigates whether regular (KF) and extended Kalman filters (eKF), derived directly from Hill-type muscle mechanics equations, can be used as real-time muscle state estimators for isometric contractions using raw electromyography signals (EMG) as the only available measurement. The estimators' amplitude error, computational cost, filtering lags and smoothness are compared with usual EMG-driven analysis, performed offline, by integrating the nonlinear Hill-type muscle model differential equations (offline simulations-OS). EMG activity of the three triceps surae components (soleus, gastrocnemius medialis and gastrocnemius lateralis), in three torque levels, was collected for ten subjects. The actualization interval (AI) between two updates of the KF and eKF was also varied. The results show that computational costs are significantly reduced (70x for KF and 17[Formula: see text] for eKF). The filtering lags presented sharp linear relationships with the AI (0-300 ms), depending on the state and activation level. Under maximum excitation, amplitude errors varied in the range 10-24% for activation, 5-8% for tendon force and 1.4-1.8% for muscle length, reducing linearly with the excitation level. Smoothness, measured by the ratio between the average standard variations of KF/eKF and OS estimations, was greatly reduced for activation but converged exponentially to 1 for the other states by increasing AI. Compared to regular KF, extended KF does not seem to improve estimation accuracy significantly. Depending on the particular application requirements, the most appropriate KF actualization interval can be selected.

Discrimination of Parkinsonian Tremor From Essential Tremor by Voting Between Different EMG Signal Processing Techniques

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A Hossen

2014-06-01

Full Text Available Parkinson's disease (PD and essential tremor (ET are the two most common disorders that cause involuntary muscle shaking movements, or what is called "tremor”. PD is a neurodegenerative disease caused by the loss of dopamine receptors which control and adjust the movement of the body. On the other hand, ET is a neurological movement disorder which also causes tremors and shaking, but it is not related to dopamine receptor loss; it is simply a tremor. The differential diagnosis between these two disorders is sometimes difficult to make clinically because of the similarities of their symptoms; additionally, the available tests are complex and expensive. Thus, the objective of this paper is to discriminate between these two disorders with simpler, cheaper and easier ways by using electromyography (EMG signal processing techniques. EMG and accelerometer records of 39 patients with PD and 41 with ET were acquired from the Hospital of Kiel University in Germany and divided into a trial group and a test group. Three main techniques were applied: the wavelet-based soft-decision technique, statistical signal characterization (SSC of the spectrum of the signal, and SSC of the amplitude variation of the Hilbert transform. The first technique resulted in a discrimination efficiency of 80% on the trial set and 85% on the test set. The second technique resulted in an efficiency of 90% on the trial set and 82.5% on the test set. The third technique resulted in an 87.5% efficiency on the trial set and 65.5% efficiency on the test set. Lastly, a final vote was done to finalize the discrimination using these three techniques, and as a result of the vote, accuracies of 92.5%, 85.0% and 88.75% were obtained on the trial data, test data and total data, respectively.

[Surface electromyography signal classification using gray system theory].

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Xie, Hongbo; Ma, Congbin; Wang, Zhizhong; Huang, Hai

2004-12-01

A new method based on gray correlation was introduced to improve the identification rate in artificial limb. The electromyography (EMG) signal was first transformed into time-frequency domain by wavelet transform. Singular value decomposition (SVD) was then used to extract feature vector from the wavelet coefficient for pattern recognition. The decision was made according to the maximum gray correlation coefficient. Compared with neural network recognition, this robust method has an almost equivalent recognition rate but much lower computation costs and less training samples.

EMG-Torque correction on Human Upper extremity using Evolutionary Computation

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JL, Veronica; Parasuraman, S.; Khan, M. K. A. Ahamed; Jeba DSingh, Kingsly

2016-09-01

There have been many studies indicating that control system of rehabilitative robot plays an important role in determining the outcome of the therapy process. Existing works have done the prediction of feedback signal in the controller based on the kinematics parameters and EMG readings of upper limb's skeletal system. Kinematics and kinetics based control signal system is developed by reading the output of the sensors such as position sensor, orientation sensor and F/T (Force/Torque) sensor and there readings are to be compared with the preceding measurement to decide on the amount of assistive force. There are also other works that incorporated the kinematics parameters to calculate the kinetics parameters via formulation and pre-defined assumptions. Nevertheless, these types of control signals analyze the movement of the upper limb only based on the movement of the upper joints. They do not anticipate the possibility of muscle plasticity. The focus of the paper is to make use of the kinematics parameters and EMG readings of skeletal system to predict the individual torque of upper extremity's joints. The surface EMG signals are fed into different mathematical models so that these data can be trained through Genetic Algorithm (GA) to find the best correlation between EMG signals and torques acting on the upper limb's joints. The estimated torque attained from the mathematical models is called simulated output. The simulated output will then be compared with the actual individual joint which is calculated based on the real time kinematics parameters of the upper movement of the skeleton when the muscle cells are activated. The findings from this contribution are extended into the development of the active control signal based controller for rehabilitation robot.

Evaluation of methods for extraction of the volitional EMG in dynamic hybrid muscle activation

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Mizrahi Joseph

2006-11-01

Full Text Available Abstract Background Hybrid muscle activation is a modality used for muscle force enhancement, in which muscle contraction is generated from two different excitation sources: volitional and external, by means of electrical stimulation (ES. Under hybrid activation, the overall EMG signal is the combination of the volitional and ES-induced components. In this study, we developed a computational scheme to extract the volitional EMG envelope from the overall dynamic EMG signal, to serve as an input signal for control purposes, and for evaluation of muscle forces. Methods A "synthetic" database was created from in-vivo experiments on the Tibialis Anterior of the right foot to emulate hybrid EMG signals, including the volitional and induced components. The database was used to evaluate the results obtained from six signal processing schemes, including seven different modules for filtration, rectification and ES component removal. The schemes differed from each other by their module combinations, as follows: blocking window only, comb filter only, blocking window and comb filter, blocking window and peak envelope, comb filter and peak envelope and, finally, blocking window, comb filter and peak envelope. Results and conclusion The results showed that the scheme including all the modules led to an excellent approximation of the volitional EMG envelope, as extracted from the hybrid signal, and underlined the importance of the artifact blocking window module in the process. The results of this work have direct implications on the development of hybrid muscle activation rehabilitation systems for the enhancement of weakened muscles.

Evaluation of EMG processing techniques using Information Theory

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Felice Carmelo J

2010-11-01

Full Text Available Abstract Background Electromyographic signals can be used in biomedical engineering and/or rehabilitation field, as potential sources of control for prosthetics and orthotics. In such applications, digital processing techniques are necessary to follow efficient and effectively the changes in the physiological characteristics produced by a muscular contraction. In this paper, two methods based on information theory are proposed to evaluate the processing techniques. Methods These methods determine the amount of information that a processing technique is able to extract from EMG signals. The processing techniques evaluated with these methods were: absolute mean value (AMV, RMS values, variance values (VAR and difference absolute mean value (DAMV. EMG signals from the middle deltoid during abduction and adduction movement of the arm in the scapular plane was registered, for static and dynamic contractions. The optimal window length (segmentation, abduction and adduction movements and inter-electrode distance were also analyzed. Results Using the optimal segmentation (200 ms and 300 ms in static and dynamic contractions, respectively the best processing techniques were: RMS, AMV and VAR in static contractions, and only the RMS in dynamic contractions. Using the RMS of EMG signal, variations in the amount of information between the abduction and adduction movements were observed. Conclusions Although the evaluation methods proposed here were applied to standard processing techniques, these methods can also be considered as alternatives tools to evaluate new processing techniques in different areas of electrophysiology.

EMG-Based Continuous and Simultaneous Estimation of Arm Kinematics in Able-Bodied Individuals and Stroke Survivors

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Jie Liu

2017-08-01

Full Text Available Among the potential biological signals for human-machine interactions (brain, nerve, and muscle signals, electromyography (EMG widely used in clinical setting can be obtained non-invasively as motor commands to control movements. The aim of this study was to develop a model for continuous and simultaneous decoding of multi-joint dynamic arm movements based on multi-channel surface EMG signals crossing the joints, leading to application of myoelectrically controlled exoskeleton robots for upper-limb rehabilitation. Twenty subjects were recruited for this study including 10 stroke subjects and 10 able-bodied subjects. The subjects performed free arm reaching movements in the horizontal plane with an exoskeleton robot. The shoulder, elbow and wrist movements and surface EMG signals from six muscles crossing the three joints were recorded. A non-linear autoregressive exogenous (NARX model was developed to continuously decode the shoulder, elbow and wrist movements based solely on the EMG signals. The shoulder, elbow and wrist movements were decoded accurately based only on the EMG inputs in all the subjects, with the variance accounted for (VAF > 98% for all three joints. The proposed approach is capable of simultaneously and continuously decoding multi-joint movements of the human arm by taking into account the non-linear mappings between the muscle EMGs and joint movements, which may provide less effortful control of robotic exoskeletons for rehabilitation training of individuals with neurological disorders and arm impairment.

An Embedded, Eight Channel, Noise Canceling, Wireless, Wearable sEMG Data Acquisition System With Adaptive Muscle Contraction Detection.

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Ergeneci, Mert; Gokcesu, Kaan; Ertan, Erhan; Kosmas, Panagiotis

2018-02-01

Wearable technology has gained increasing popularity in the applications of healthcare, sports science, and biomedical engineering in recent years. Because of its convenient nature, the wearable technology is particularly useful in the acquisition of the physiological signals. Specifically, the (surface electromyography) sEMG systems, which measure the muscle activation potentials, greatly benefit from this technology in both clinical and industrial applications. However, the current wearable sEMG systems have several drawbacks including inefficient noise cancellation, insufficient measurement quality, and difficult integration to customized applications. Additionally, none of these sEMG data acquisition systems can detect sEMG signals (i.e., contractions), which provides a valuable environment for further studies such as human machine interaction, gesture recognition, and fatigue tracking. To this end, we introduce an embedded, eight channel, noise canceling, wireless, wearable sEMG data acquisition system with adaptive muscle contraction detection. Our design consists of two stages, which are the sEMG sensors and the multichannel data acquisition unit. For the first stage, we propose a low cost, dry, and active sEMG sensor that captures the muscle activation potentials, a data acquisition unit that evaluates these captured multichannel sEMG signals and transmits them to a user interface. In the data acquisition unit, the sEMG signals are processed through embedded, adaptive methods in order to reject the power line noise and detect the muscle contractions. Through extensive experiments, we demonstrate that our sEMG sensor outperforms a widely used commercially available product and our data acquisition system achieves 4.583 dB SNR gain with accuracy in the detection of the contractions.

Muscle Performance Investigated With a Novel Smart Compression Garment Based on Pressure Sensor Force Myography and Its Validation Against EMG

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Aaron Belbasis

2018-04-01

Full Text Available Muscle activity and fatigue performance parameters were obtained and compared between both a smart compression garment and the gold-standard, a surface electromyography (EMG system during high-speed cycling in seven participants. The smart compression garment, based on force myography (FMG, comprised of integrated pressure sensors that were sandwiched between skin and garment, located on five thigh muscles. The muscle activity was assessed by means of crank cycle diagrams (polar plots that displayed the muscle activity relative to the crank cycle. The fatigue was assessed by means of the median frequency of the power spectrum of the EMG signal; the fractal dimension (FD of the EMG signal; and the FD of the pressure signal. The smart compression garment returned performance parameters (muscle activity and fatigue comparable to the surface EMG. The major differences were that the EMG measured the electrical activity, whereas the pressure sensor measured the mechanical activity. As such, there was a phase shift between electrical and mechanical signals, with the electrical signals preceding the mechanical counterparts in most cases. This is specifically pronounced in high-speed cycling. The fatigue trend over the duration of the cycling exercise was clearly reflected in the fatigue parameters (FDs and median frequency obtained from pressure and EMG signals. The fatigue parameter of the pressure signal (FD showed a higher time dependency (R2 = 0.84 compared to the EMG signal. This reflects that the pressure signal puts more emphasis on the fatigue as a function of time rather than on the origin of fatigue (e.g., peripheral or central fatigue. In light of the high-speed activity results, caution should be exerted when using data obtained from EMG for biomechanical models. In contrast to EMG data, activity data obtained from FMG are considered more appropriate and accurate as an input for biomechanical modeling as they truly reflect the mechanical

Muscle Performance Investigated With a Novel Smart Compression Garment Based on Pressure Sensor Force Myography and Its Validation Against EMG.

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Belbasis, Aaron; Fuss, Franz Konstantin

2018-01-01

Muscle activity and fatigue performance parameters were obtained and compared between both a smart compression garment and the gold-standard, a surface electromyography (EMG) system during high-speed cycling in seven participants. The smart compression garment, based on force myography (FMG), comprised of integrated pressure sensors that were sandwiched between skin and garment, located on five thigh muscles. The muscle activity was assessed by means of crank cycle diagrams (polar plots) that displayed the muscle activity relative to the crank cycle. The fatigue was assessed by means of the median frequency of the power spectrum of the EMG signal; the fractal dimension (FD) of the EMG signal; and the FD of the pressure signal. The smart compression garment returned performance parameters (muscle activity and fatigue) comparable to the surface EMG. The major differences were that the EMG measured the electrical activity, whereas the pressure sensor measured the mechanical activity. As such, there was a phase shift between electrical and mechanical signals, with the electrical signals preceding the mechanical counterparts in most cases. This is specifically pronounced in high-speed cycling. The fatigue trend over the duration of the cycling exercise was clearly reflected in the fatigue parameters (FDs and median frequency) obtained from pressure and EMG signals. The fatigue parameter of the pressure signal (FD) showed a higher time dependency ( R 2 = 0.84) compared to the EMG signal. This reflects that the pressure signal puts more emphasis on the fatigue as a function of time rather than on the origin of fatigue (e.g., peripheral or central fatigue). In light of the high-speed activity results, caution should be exerted when using data obtained from EMG for biomechanical models. In contrast to EMG data, activity data obtained from FMG are considered more appropriate and accurate as an input for biomechanical modeling as they truly reflect the mechanical muscle

Grip Force and 3D Push-Pull Force Estimation Based on sEMG and GRNN

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Changcheng Wu

2017-06-01

Full Text Available The estimation of the grip force and the 3D push-pull force (push and pull force in the three dimension space from the electromyogram (EMG signal is of great importance in the dexterous control of the EMG prosthetic hand. In this paper, an action force estimation method which is based on the eight channels of the surface EMG (sEMG and the Generalized Regression Neural Network (GRNN is proposed to meet the requirements of the force control of the intelligent EMG prosthetic hand. Firstly, the experimental platform, the acquisition of the sEMG, the feature extraction of the sEMG and the construction of GRNN are described. Then, the multi-channels of the sEMG when the hand is moving are captured by the EMG sensors attached on eight different positions of the arm skin surface. Meanwhile, a grip force sensor and a three dimension force sensor are adopted to measure the output force of the human's hand. The characteristic matrix of the sEMG and the force signals are used to construct the GRNN. The mean absolute value and the root mean square of the estimation errors, the correlation coefficients between the actual force and the estimated force are employed to assess the accuracy of the estimation. Analysis of variance (ANOVA is also employed to test the difference of the force estimation. The experiments are implemented to verify the effectiveness of the proposed estimation method and the results show that the output force of the human's hand can be correctly estimated by using sEMG and GRNN method.

Evaluation of higher order statistics parameters for multi channel sEMG using different force levels.

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Naik, Ganesh R; Kumar, Dinesh K

2011-01-01

The electromyograpy (EMG) signal provides information about the performance of muscles and nerves. The shape of the muscle signal and motor unit action potential (MUAP) varies due to the movement of the position of the electrode or due to changes in contraction level. This research deals with evaluating the non-Gaussianity in Surface Electromyogram signal (sEMG) using higher order statistics (HOS) parameters. To achieve this, experiments were conducted for four different finger and wrist actions at different levels of Maximum Voluntary Contractions (MVCs). Our experimental analysis shows that at constant force and for non-fatiguing contractions, probability density functions (PDF) of sEMG signals were non-Gaussian. For lesser MVCs (below 30% of MVC) PDF measures tends to be Gaussian process. The above measures were verified by computing the Kurtosis values for different MVCs.

Effectiveness of the Wavelet Transform on the Surface EMG to Understand the Muscle Fatigue During Walk

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Hussain, M. S.; Mamun, Md.

2012-01-01

Muscle fatigue is the decline in ability of a muscle to create force. Electromyography (EMG) is a medical technique for measuring muscle response to nervous stimulation. During a sustained muscle contraction, the power spectrum of the EMG shifts towards lower frequencies. These effects are due to muscle fatigue. Muscle fatigue is often a result of unhealthy work practice. In this research, the effectiveness of the wavelet transform applied to the surface EMG (SEMG) signal as a means of understanding muscle fatigue during walk is presented. Power spectrum and bispectrum analysis on the EMG signal getting from right rectus femoris muscle is executed utilizing various wavelet functions (WFs). It is possible to recognize muscle fatigue appreciably with the proper choice of the WF. The outcome proves that the most momentous changes in the EMG power spectrum are symbolized by WF Daubechies45. Moreover, this research has compared bispectrum properties to the other WFs. To determine muscle fatigue during gait, Daubechies45 is used in this research to analyze the SEMG signal.

Steering a Tractor by Means of an EMG-Based Human-Machine Interface

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Sergio Alonso-Garcia

2011-07-01

Full Text Available An electromiographic (EMG-based human-machine interface (HMI is a communication pathway between a human and a machine that operates by means of the acquisition and processing of EMG signals. This article explores the use of EMG-based HMIs in the steering of farm tractors. An EPOC, a low-cost human-computer interface (HCI from the Emotiv Company, was employed. This device, by means of 14 saline sensors, measures and processes EMG and electroencephalographic (EEG signals from the scalp of the driver. In our tests, the HMI took into account only the detection of four trained muscular events on the driver’s scalp: eyes looking to the right and jaw opened, eyes looking to the right and jaw closed, eyes looking to the left and jaw opened, and eyes looking to the left and jaw closed. The EMG-based HMI guidance was compared with manual guidance and with autonomous GPS guidance. A driver tested these three guidance systems along three different trajectories: a straight line, a step, and a circumference. The accuracy of the EMG-based HMI guidance was lower than the accuracy obtained by manual guidance, which was lower in turn than the accuracy obtained by the autonomous GPS guidance; the computed standard deviations of error to the desired trajectory in the straight line were 16 cm, 9 cm, and 4 cm, respectively. Since the standard deviation between the manual guidance and the EMG-based HMI guidance differed only 7 cm, and this difference is not relevant in agricultural steering, it can be concluded that it is possible to steer a tractor by an EMG-based HMI with almost the same accuracy as with manual steering.

Signal classification using global dynamical models, Part I: Theory

International Nuclear Information System (INIS)

Kadtke, J.; Kremliovsky, M.

1996-01-01

Detection and classification of signals is one of the principal areas of signal processing, and the utilization of nonlinear information has long been considered as a way of improving performance beyond standard linear (e.g. spectral) techniques. Here, we develop a method for using global models of chaotic dynamical systems theory to define a signal classification processing chain, which is sensitive to nonlinear correlations in the data. We use it to demonstrate classification in high noise regimes (negative SNR), and argue that classification probabilities can be directly computed from ensemble statistics in the model coefficient space. We also develop a modification for non-stationary signals (i.e. transients) using non-autonomous ODEs. In Part II of this paper, we demonstrate the analysis on actual open ocean acoustic data from marine biologics. copyright 1996 American Institute of Physics

Effect of vibrotactile feedback on an EMG-based proportional cursor control system.

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Li, Shunchong; Chen, Xingyu; Zhang, Dingguo; Sheng, Xinjun; Zhu, Xiangyang

2013-01-01

Surface electromyography (sEMG) has been introduced into the bio-mechatronics systems, however, most of them are lack of the sensory feedback. In this paper, the effect of vibrotactile feedback for a myoelectric cursor control system is investigated quantitatively. Simultaneous and proportional control signals are extracted from EMG using a muscle synergy model. Different types of feedback including vibrotactile feedback and visual feedback are added, assessed and compared with each other. The results show that vibrotactile feedback is capable of improving the performance of EMG-based human machine interface.

EMG-Based Estimation of Limb Movement Using Deep Learning With Recurrent Convolutional Neural Networks.

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Xia, Peng; Hu, Jie; Peng, Yinghong

2017-10-25

A novel model based on deep learning is proposed to estimate kinematic information for myoelectric control from multi-channel electromyogram (EMG) signals. The neural information of limb movement is embedded in EMG signals that are influenced by all kinds of factors. In order to overcome the negative effects of variability in signals, the proposed model employs the deep architecture combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The EMG signals are transformed to time-frequency frames as the input to the model. The limb movement is estimated by the model that is trained with the gradient descent and backpropagation procedure. We tested the model for simultaneous and proportional estimation of limb movement in eight healthy subjects and compared it with support vector regression (SVR) and CNNs on the same data set. The experimental studies show that the proposed model has higher estimation accuracy and better robustness with respect to time. The combination of CNNs and RNNs can improve the model performance compared with using CNNs alone. The model of deep architecture is promising in EMG decoding and optimization of network structures can increase the accuracy and robustness. © 2017 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Real-time simultaneous and proportional myoelectric control using intramuscular EMG

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Smith, Lauren H.; Kuiken, Todd A.; Hargrove, Levi J.

2014-12-01

Objective. Myoelectric prostheses use electromyographic (EMG) signals to control movement of prosthetic joints. Clinically available myoelectric control strategies do not allow simultaneous movement of multiple degrees of freedom (DOFs); however, the use of implantable devices that record intramuscular EMG signals could overcome this constraint. The objective of this study was to evaluate the real-time simultaneous control of three DOFs (wrist rotation, wrist flexion/extension, and hand open/close) using intramuscular EMG. Approach. We evaluated task performance of five able-bodied subjects in a virtual environment using two control strategies with fine-wire EMG: (i) parallel dual-site differential control, which enabled simultaneous control of three DOFs and (ii) pattern recognition control, which required sequential control of DOFs. Main results. Over the course of the experiment, subjects using parallel dual-site control demonstrated increased use of simultaneous control and improved performance in a Fitts’ Law test. By the end of the experiment, performance using parallel dual-site control was significantly better (up to a 25% increase in throughput) than when using sequential pattern recognition control for tasks requiring multiple DOFs. The learning trends with parallel dual-site control suggested that further improvements in performance metrics were possible. Subjects occasionally experienced difficulty in performing isolated single-DOF movements with parallel dual-site control but were able to accomplish related Fitts’ Law tasks with high levels of path efficiency. Significance. These results suggest that intramuscular EMG, used in a parallel dual-site configuration, can provide simultaneous control of a multi-DOF prosthetic wrist and hand and may outperform current methods that enforce sequential control.

Learning an EMG Controlled Game: Task-Specific Adaptations and Transfer.

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van Dijk, Ludger; van der Sluis, Corry K; van Dijk, Hylke W; Bongers, Raoul M

2016-01-01

Video games that aim to improve myoelectric control (myogames) are gaining popularity and are often part of the rehabilitation process following an upper limb amputation. However, direct evidence for their effect on prosthetic skill is limited. This study aimed to determine whether and how myogaming improves EMG control and whether performance improvements transfer to a prosthesis-simulator task. Able-bodied right-handed participants (N = 28) were randomly assigned to 1 of 2 groups. The intervention group was trained to control a video game (Breakout-EMG) using the myosignals of wrist flexors and extensors. Controls played a regular Mario computer game. Both groups trained 20 minutes a day for 4 consecutive days. Before and after training, two tests were conducted: one level of the Breakout-EMG game, and grasping objects with a prosthesis-simulator. Results showed a larger increase of in-game accuracy for the Breakout-EMG group than for controls. The Breakout-EMG group moreover showed increased adaptation of the EMG signal to the game. No differences were found in using a prosthesis-simulator. This study demonstrated that myogames lead to task-specific myocontrol skills. Transfer to a prosthesis task is therefore far from easy. We discuss several implications for future myogame designs.

Modulation of EMG-EMG Coherence in a Choice Stepping Task

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Ippei Nojima

2018-02-01

Full Text Available The voluntary step execution task is a popular measure for identifying fall risks among elderly individuals in the community setting because most falls have been reported to occur during movement. However, the neurophysiological functions during this movement are not entirely understood. Here, we used electromyography (EMG to explore the relationship between EMG-EMG coherence, which reflects common oscillatory drive to motoneurons, and motor performance associated with stepping tasks: simple reaction time (SRT and choice reaction time (CRT tasks. Ten healthy elderly adults participated in the study. Participants took a single step forward in response to a visual imperative stimulus. EMG-EMG coherence was analyzed for 1000 ms before the presentation of the stimulus (stationary standing position from proximal and distal tibialis anterior (TA and soleus (SOL muscles. The main result showed that all paired EMG-EMG coherences in the alpha and beta frequency bands were greater in the SRT than the CRT task. This finding suggests that the common oscillatory drive to the motoneurons during the SRT task occurred prior to taking a step, whereas the lower value of corticospinal activity during the CRT task prior to taking a step may indicate an involvement of inhibitory activity, which is consistent with observations from our previous study (Watanabe et al., 2016. Furthermore, the beta band coherence in intramuscular TA tended to positively correlate with the number of performance errors that are associated with fall risks in the CRT task, suggesting that a reduction in the inhibitory activity may result in a decrease of stepping performance. These findings could advance the understanding of the neurophysiological features of postural adjustments in elderly individuals.

Modified DCTNet for audio signals classification

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Xian, Yin; Pu, Yunchen; Gan, Zhe; Lu, Liang; Thompson, Andrew

2016-10-01

In this paper, we investigate DCTNet for audio signal classification. Its output feature is related to Cohen's class of time-frequency distributions. We introduce the use of adaptive DCTNet (A-DCTNet) for audio signals feature extraction. The A-DCTNet applies the idea of constant-Q transform, with its center frequencies of filterbanks geometrically spaced. The A-DCTNet is adaptive to different acoustic scales, and it can better capture low frequency acoustic information that is sensitive to human audio perception than features such as Mel-frequency spectral coefficients (MFSC). We use features extracted by the A-DCTNet as input for classifiers. Experimental results show that the A-DCTNet and Recurrent Neural Networks (RNN) achieve state-of-the-art performance in bird song classification rate, and improve artist identification accuracy in music data. They demonstrate A-DCTNet's applicability to signal processing problems.

EMGD-FE: an open source graphical user interface for estimating isometric muscle forces in the lower limb using an EMG-driven model.

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Menegaldo, Luciano Luporini; de Oliveira, Liliam Fernandes; Minato, Kin K

2014-04-04

This paper describes the "EMG Driven Force Estimator (EMGD-FE)", a Matlab® graphical user interface (GUI) application that estimates skeletal muscle forces from electromyography (EMG) signals. Muscle forces are obtained by numerically integrating a system of ordinary differential equations (ODEs) that simulates Hill-type muscle dynamics and that utilises EMG signals as input. In the current version, the GUI can estimate the forces of lower limb muscles executing isometric contractions. Muscles from other parts of the body can be tested as well, although no default values for model parameters are provided. To achieve accurate evaluations, EMG collection is performed simultaneously with torque measurement from a dynamometer. The computer application guides the user, step-by-step, to pre-process the raw EMG signals, create inputs for the muscle model, numerically integrate the ODEs and analyse the results. An example of the application's functions is presented using the quadriceps femoris muscle. Individual muscle force estimations for the four components as well the knee isometric torque are shown. The proposed GUI can estimate individual muscle forces from EMG signals of skeletal muscles. The estimation accuracy depends on several factors, including signal collection and modelling hypothesis issues.

Power frequency spectrum analysis of surface EMG signals of upper limb muscles during elbow flexion - A comparison between healthy subjects and stroke survivors.

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Angelova, Silvija; Ribagin, Simeon; Raikova, Rositsa; Veneva, Ivanka

2018-02-01

After a stroke, motor units stop working properly and large, fast-twitch units are more frequently affected. Their impaired functions can be investigated during dynamic tasks using electromyographic (EMG) signal analysis. The aim of this paper is to investigate changes in the parameters of the power/frequency function during elbow flexion between affected, non-affected, and healthy muscles. Fifteen healthy subjects and ten stroke survivors participated in the experiments. Electromyographic data from 6 muscles of the upper limbs during elbow flexion were filtered and normalized to the amplitudes of EMG signals during maximal isometric tasks. The moments when motion started and when the flexion angle reached its maximal value were found. Equal intervals of 0.3407 s were defined between these two moments and one additional interval before the start of the flexion (first one) was supplemented. For each of these intervals the power/frequency function of EMG signals was calculated. The mean (MNF) and median frequencies (MDF), the maximal power (MPw) and the area under the power function (APw) were calculated. MNF was always higher than MDF. A significant decrease in these frequencies was found in only three post-stroke survivors. The frequencies in the first time interval were nearly always the highest among all intervals. The maximal power was nearly zero during first time interval and increased during the next ones. The largest values of MPw and APw were found for the flexor muscles and they increased for the muscles of the affected arm compared to the non-affected one of stroke survivors. Copyright © 2017 Elsevier Ltd. All rights reserved.

Subsurface event detection and classification using Wireless Signal Networks.

Science.gov (United States)

Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T

2012-11-05

Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events.

Computational Intelligence Based Data Fusion Algorithm for Dynamic sEMG and Skeletal Muscle Force Modelling

Energy Technology Data Exchange (ETDEWEB)

Chandrasekhar Potluri,; Madhavi Anugolu; Marco P. Schoen; D. Subbaram Naidu

2013-08-01

In this work, an array of three surface Electrography (sEMG) sensors are used to acquired muscle extension and contraction signals for 18 healthy test subjects. The skeletal muscle force is estimated using the acquired sEMG signals and a Non-linear Wiener Hammerstein model, relating the two signals in a dynamic fashion. The model is obtained from using System Identification (SI) algorithm. The obtained force models for each sensor are fused using a proposed fuzzy logic concept with the intent to improve the force estimation accuracy and resilience to sensor failure or misalignment. For the fuzzy logic inference system, the sEMG entropy, the relative error, and the correlation of the force signals are considered for defining the membership functions. The proposed fusion algorithm yields an average of 92.49% correlation between the actual force and the overall estimated force output. In addition, the proposed fusionbased approach is implemented on a test platform. Experiments indicate an improvement in finger/hand force estimation.

sEMG feature evaluation for identification of elbow angle resolution in graded arm movement.

Science.gov (United States)

Castro, Maria Claudia F; Colombini, Esther L; Aquino, Plinio T; Arjunan, Sridhar P; Kumar, Dinesh K

2014-11-25

Automatic and accurate identification of elbow angle from surface electromyogram (sEMG) is essential for myoelectric controlled upper limb exoskeleton systems. This requires appropriate selection of sEMG features, and identifying the limitations of such a system.This study has demonstrated that it is possible to identify three discrete positions of the elbow; full extension, right angle, and mid-way point, with window size of only 200 milliseconds. It was seen that while most features were suitable for this purpose, Power Spectral Density Averages (PSD-Av) performed best. The system correctly classified the sEMG against the elbow angle for 100% cases when only two discrete positions (full extension and elbow at right angle) were considered, while correct classification was 89% when there were three discrete positions. However, sEMG was unable to accurately determine the elbow position when five discrete angles were considered. It was also observed that there was no difference for extension or flexion phases.

A Control Strategy with Tactile Perception Feedback for EMG Prosthetic Hand

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Changcheng Wu

2015-01-01

Full Text Available To improve the control effectiveness and make the prosthetic hand not only controllable but also perceivable, an EMG prosthetic hand control strategy was proposed in this paper. The control strategy consists of EMG self-learning motion recognition, backstepping controller with stiffness fuzzy observation, and force tactile representation. EMG self-learning motion recognition is used to reduce the influence on EMG signals caused by the uncertainty of the contacting position of the EMG sensors. Backstepping controller with stiffness fuzzy observation is used to realize the position control and grasp force control. Velocity proportional control in free space and grasp force tracking control in restricted space can be realized by the same controller. The force tactile representation helps the user perceive the states of the prosthetic hand. Several experiments were implemented to verify the effect of the proposed control strategy. The results indicate that the proposed strategy has effectiveness. During the experiments, the comments of the participants show that the proposed strategy is a better choice for amputees because of the improved controllability and perceptibility.

Biological signals classification and analysis

CERN Document Server

Kiasaleh, Kamran

2015-01-01

This authored monograph presents key aspects of signal processing analysis in the biomedical arena. Unlike wireless communication systems, biological entities produce signals with underlying nonlinear, chaotic nature that elude classification using the standard signal processing techniques, which have been developed over the past several decades for dealing primarily with standard communication systems. This book separates what is random from that which appears to be random, and yet is truly deterministic with random appearance. At its core, this work gives the reader a perspective on biomedical signals and the means to classify and process such signals. In particular, a review of random processes along with means to assess the behavior of random signals is also provided. The book also includes a general discussion of biological signals in order to demonstrate the inefficacy of the well-known techniques to correctly extract meaningful information from such signals. Finally, a thorough discussion of recently ...

Simulation of facial expressions using person-specific sEMG signals controlling a biomechanical face model.

Science.gov (United States)

Eskes, Merijn; Balm, Alfons J M; van Alphen, Maarten J A; Smeele, Ludi E; Stavness, Ian; van der Heijden, Ferdinand

2018-01-01

Functional inoperability in advanced oral cancer is difficult to assess preoperatively. To assess functions of lips and tongue, biomechanical models are required. Apart from adjusting generic models to individual anatomy, muscle activation patterns (MAPs) driving patient-specific functional movements are necessary to predict remaining functional outcome. We aim to evaluate how volunteer-specific MAPs derived from surface electromyographic (sEMG) signals control a biomechanical face model. Muscle activity of seven facial muscles in six volunteers was measured bilaterally with sEMG. A triple camera set-up recorded 3D lip movement. The generic face model in ArtiSynth was adapted to our needs. We controlled the model using the volunteer-specific MAPs. Three activation strategies were tested: activating all muscles [Formula: see text], selecting the three muscles showing highest muscle activity bilaterally [Formula: see text]-this was calculated by taking the mean of left and right muscles and then selecting the three with highest variance-and activating the muscles considered most relevant per instruction [Formula: see text], bilaterally. The model's lip movement was compared to the actual lip movement performed by the volunteers, using 3D correlation coefficients [Formula: see text]. The correlation coefficient between simulations and measurements with [Formula: see text] resulted in a median [Formula: see text] of 0.77. [Formula: see text] had a median [Formula: see text] of 0.78, whereas with [Formula: see text] the median [Formula: see text] decreased to 0.45. We demonstrated that MAPs derived from noninvasive sEMG measurements can control movement of the lips in a generic finite element face model with a median [Formula: see text] of 0.78. Ultimately, this is important to show the patient-specific residual movement using the patient's own MAPs. When the required treatment tools and personalisation techniques for geometry and anatomy become available, this may

Novel Feature Modelling the Prediction and Detection of sEMG Muscle Fatigue towards an Automated Wearable System

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Mohamed R. Al-Mulla

2010-05-01

Full Text Available Surface Electromyography (sEMG activity of the biceps muscle was recorded from ten subjects performing isometric contraction until fatigue. A novel feature (1D spectro_std was used to extract the feature that modeled three classes of fatigue, which enabled the prediction and detection of fatigue. Initial results of class separation were encouraging, discriminating between the three classes of fatigue, a longitudinal classification on Non-Fatigue and Transition-to-Fatigue shows 81.58% correct classification with accuracy 0.74 of correct predictions while the longitudinal classification on Transition-to-Fatigue and Fatigue showed lower average correct classification of 66.51% with a positive classification accuracy 0.73 of correct prediction. Comparison of the 1D spectro_std with other sEMG fatigue features on the same dataset show a significant improvement in classification, where results show a significant 20.58% (p < 0.01 improvement when using the 1D spectro_std to classify Non-Fatigue and Transition-to-Fatigue. In classifying Transition-to-Fatigue and Fatigue results also show a significant improvement over the other features giving 8.14% (p < 0.05 on average of all compared features.

Neuromuscular interfacing: establishing an EMG-driven model for the human elbow joint.

Science.gov (United States)

Pau, James W L; Xie, Shane S Q; Pullan, Andrew J

2012-09-01

Assistive devices aim to mitigate the effects of physical disability by aiding users to move their limbs or by rehabilitating through therapy. These devices are commonly embodied by robotic or exoskeletal systems that are still in development and use the electromyographic (EMG) signal to determine user intent. Not much focus has been placed on developing a neuromuscular interface (NI) that solely relies on the EMG signal, and does not require modifications to the end user's state to enhance the signal (such as adding weights). This paper presents the development of a flexible, physiological model for the elbow joint that is leading toward the implementation of an NI, which predicts joint motion from EMG signals for both able-bodied and less-abled users. The approach uses musculotendon models to determine muscle contraction forces, a proposed musculoskeletal model to determine total joint torque, and a kinematic model to determine joint rotational kinematics. After a sensitivity analysis and tuning using genetic algorithms, subject trials yielded an average root-mean-square error of 6.53° and 22.4° for a single cycle and random cycles of movement of the elbow joint, respectively. This helps us to validate the elbow model and paves the way toward the development of an NI.

Comparison of algorithms to quantify muscle fatigue in upper limb muscles based on sEMG signals.

Science.gov (United States)

Kahl, Lorenz; Hofmann, Ulrich G

2016-11-01

This work compared the performance of six different fatigue detection algorithms quantifying muscle fatigue based on electromyographic signals. Surface electromyography (sEMG) was obtained by an experiment from upper arm contractions at three different load levels from twelve volunteers. Fatigue detection algorithms mean frequency (MNF), spectral moments ratio (SMR), the wavelet method WIRM1551, sample entropy (SampEn), fuzzy approximate entropy (fApEn) and recurrence quantification analysis (RQA%DET) were calculated. The resulting fatigue signals were compared considering the disturbances incorporated in fatiguing situations as well as according to the possibility to differentiate the load levels based on the fatigue signals. Furthermore we investigated the influence of the electrode locations on the fatigue detection quality and whether an optimized channel set is reasonable. The results of the MNF, SMR, WIRM1551 and fApEn algorithms fell close together. Due to the small amount of subjects in this study significant differences could not be found. In terms of disturbances the SMR algorithm showed a slight tendency to out-perform the others. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

Subsurface Event Detection and Classification Using Wireless Signal Networks

Directory of Open Access Journals (Sweden)

Muhannad T. Suleiman

2012-11-01

Full Text Available Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs. The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events.

Trapezius muscle EMG as predictor of mental stress

NARCIS (Netherlands)

Wijsman, J.L.P; Grundlehner, B.; Penders, J.; Hermens, Hermanus J.

Stress is a growing problem in society and can cause musculoskeletal complaints. It would be useful to measure stress for prevention of stress-related health problems. An experiment is described in which EMG signals of the upper trapezius muscle were measured with a wireless system during three

Heart rate variability (HRV) and muscular system activity (EMG) in cases of crash threat during simulated driving of a passenger car.

Science.gov (United States)

Zużewicz, Krystyna; Roman-Liu, Danuta; Konarska, Maria; Bartuzi, Paweł; Matusiak, Krzysztof; Korczak, Dariusz; Lozia, Zbigniew; Guzek, Marek

2013-10-01

The aim of the study was to verify whether simultaneous responses from the muscular and circulatory system occur in the driver's body under simulated conditions of a crash threat. The study was carried out in a passenger car driving simulator. The crash was included in the driving test scenario developed in an urban setting. In the group of 22 young male subjects, two physiological signals - ECG and EMG were continuously recorded. The length of the RR interval in the ECG signal was assessed. A HRV analysis was performed in the time and frequency domains for 1-minute record segments at rest (seated position), during undisturbed driving as well as during and several minutes after the crash. For the left and right side muscles: m. trapezius (TR) and m. flexor digitorum superficialis (FDS), the EMG signal amplitude was determined. The percentage of maximal voluntary contraction (MVC) was compared during driving and during the crash. As for the ECG signal, it was found that in most of the drivers changes occurred in the parameter values reflecting HRV in the time domain. Significant changes were noted in the mean length of RR intervals (mRR). As for the EMG signal, the changes in the amplitude concerned the signal recorded from the FDS muscle. The changes in ECG and EMG were simultaneous in half of the cases. Such parameters as mRR (ECG signal) and FDS-L amplitude (EMG signal) were the responses to accident risk. Under simulated conditions, responses from the circulatory and musculoskeletal systems are not always simultaneous. The results indicate that a more complete driver's response to a crash in road traffic is obtained based on parallel recording of two physiological signals (ECG and EMG).

EMG analysis tuned for determining the timing and level of activation in different motor units.

Science.gov (United States)

Lee, Sabrina S M; Miara, Maria de Boef; Arnold, Allison S; Biewener, Andrew A; Wakeling, James M

2011-08-01

Recruitment patterns and activation dynamics of different motor units greatly influence the temporal pattern and magnitude of muscle force development, yet these features are not often considered in muscle models. The purpose of this study was to characterize the recruitment and activation dynamics of slow and fast motor units from electromyographic (EMG) recordings and twitch force profiles recorded directly from animal muscles. EMG and force data from the gastrocnemius muscles of seven goats were recorded during in vivo tendon-tap reflex and in situ nerve stimulation experiments. These experiments elicited EMG signals with significant differences in frequency content (p<0.001). The frequency content was characterized using wavelet and principal components analysis, and optimized wavelets with centre frequencies, 149.94 Hz and 323.13 Hz, were obtained. The optimized wavelets were used to calculate the EMG intensities and, with the reconstructed twitch force profiles, to derive transfer functions for slow and fast motor units that estimate the activation state of the muscle from the EMG signal. The resulting activation-deactivation time constants gave r values of 0.98-0.99 between the activation state and the force profiles. This work establishes a framework for developing improved muscle models that consider the intrinsic properties of slow and fast fibres within a mixed muscle, and that can more accurately predict muscle force output from EMG. Copyright © 2011 Elsevier Ltd. All rights reserved.

Fractal based modelling and analysis of electromyography (EMG) to identify subtle actions.

Science.gov (United States)

Arjunan, Sridhar P; Kumar, Dinesh K

2007-01-01

The paper reports the use of fractal theory and fractal dimension to study the non-linear properties of surface electromyogram (sEMG) and to use these properties to classify subtle hand actions. The paper reports identifying a new feature of the fractal dimension, the bias that has been found to be useful in modelling the muscle activity and of sEMG. Experimental results demonstrate that the feature set consisting of bias values and fractal dimension of the recordings is suitable for classification of sEMG against the different hand gestures. The scatter plots demonstrate the presence of simple relationships of these features against the four hand gestures. The results indicate that there is small inter-experimental variation but large inter-subject variation. This may be due to differences in the size and shape of muscles for different subjects. The possible applications of this research include use in developing prosthetic hands, controlling machines and computers.

EMG Processing Based Measures of Fatigue Assessment during Manual Lifting

Directory of Open Access Journals (Sweden)

E. F. Shair

2017-01-01

Full Text Available Manual lifting is one of the common practices used in the industries to transport or move objects to a desired place. Nowadays, even though mechanized equipment is widely available, manual lifting is still considered as an essential way to perform material handling task. Improper lifting strategies may contribute to musculoskeletal disorders (MSDs, where overexertion contributes as the highest factor. To overcome this problem, electromyography (EMG signal is used to monitor the workers’ muscle condition and to find maximum lifting load, lifting height and number of repetitions that the workers are able to handle before experiencing fatigue to avoid overexertion. Past researchers have introduced several EMG processing techniques and different EMG features that represent fatigue indices in time, frequency, and time-frequency domain. The impact of EMG processing based measures in fatigue assessment during manual lifting are reviewed in this paper. It is believed that this paper will greatly benefit researchers who need a bird’s eye view of the biosignal processing which are currently available, thus determining the best possible techniques for lifting applications.

EMG Processing Based Measures of Fatigue Assessment during Manual Lifting

Science.gov (United States)

Marhaban, M. H.; Abdullah, A. R.

2017-01-01

Manual lifting is one of the common practices used in the industries to transport or move objects to a desired place. Nowadays, even though mechanized equipment is widely available, manual lifting is still considered as an essential way to perform material handling task. Improper lifting strategies may contribute to musculoskeletal disorders (MSDs), where overexertion contributes as the highest factor. To overcome this problem, electromyography (EMG) signal is used to monitor the workers' muscle condition and to find maximum lifting load, lifting height and number of repetitions that the workers are able to handle before experiencing fatigue to avoid overexertion. Past researchers have introduced several EMG processing techniques and different EMG features that represent fatigue indices in time, frequency, and time-frequency domain. The impact of EMG processing based measures in fatigue assessment during manual lifting are reviewed in this paper. It is believed that this paper will greatly benefit researchers who need a bird's eye view of the biosignal processing which are currently available, thus determining the best possible techniques for lifting applications. PMID:28303251

Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network.

Science.gov (United States)

Zhai, Xiaolong; Jelfs, Beth; Chan, Rosa H M; Tin, Chung

2017-01-01

Hand movement classification based on surface electromyography (sEMG) pattern recognition is a promising approach for upper limb neuroprosthetic control. However, maintaining day-to-day performance is challenged by the non-stationary nature of sEMG in real-life operation. In this study, we propose a self-recalibrating classifier that can be automatically updated to maintain a stable performance over time without the need for user retraining. Our classifier is based on convolutional neural network (CNN) using short latency dimension-reduced sEMG spectrograms as inputs. The pretrained classifier is recalibrated routinely using a corrected version of the prediction results from recent testing sessions. Our proposed system was evaluated with the NinaPro database comprising of hand movement data of 40 intact and 11 amputee subjects. Our system was able to achieve ~10.18% (intact, 50 movement types) and ~2.99% (amputee, 10 movement types) increase in classification accuracy averaged over five testing sessions with respect to the unrecalibrated classifier. When compared with a support vector machine (SVM) classifier, our CNN-based system consistently showed higher absolute performance and larger improvement as well as more efficient training. These results suggest that the proposed system can be a useful tool to facilitate long-term adoption of prosthetics for amputees in real-life applications.

Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm

Directory of Open Access Journals (Sweden)

E. Parvinnia

2014-01-01

Full Text Available Electroencephalogram (EEG signals are often used to diagnose diseases such as seizure, alzheimer, and schizophrenia. One main problem with the recorded EEG samples is that they are not equally reliable due to the artifacts at the time of recording. EEG signal classification algorithms should have a mechanism to handle this issue. It seems that using adaptive classifiers can be useful for the biological signals such as EEG. In this paper, a general adaptive method named weighted distance nearest neighbor (WDNN is applied for EEG signal classification to tackle this problem. This classification algorithm assigns a weight to each training sample to control its influence in classifying test samples. The weights of training samples are used to find the nearest neighbor of an input query pattern. To assess the performance of this scheme, EEG signals of thirteen schizophrenic patients and eighteen normal subjects are analyzed for the classification of these two groups. Several features including, fractal dimension, band power and autoregressive (AR model are extracted from EEG signals. The classification results are evaluated using Leave one (subject out cross validation for reliable estimation. The results indicate that combination of WDNN and selected features can significantly outperform the basic nearest-neighbor and the other methods proposed in the past for the classification of these two groups. Therefore, this method can be a complementary tool for specialists to distinguish schizophrenia disorder.

Comparison of sEMG processing methods during whole-body vibration exercise.

Science.gov (United States)

Lienhard, Karin; Cabasson, Aline; Meste, Olivier; Colson, Serge S

2015-12-01

The objective was to investigate the influence of surface electromyography (sEMG) processing methods on the quantification of muscle activity during whole-body vibration (WBV) exercises. sEMG activity was recorded while the participants performed squats on the platform with and without WBV. The spikes observed in the sEMG spectrum at the vibration frequency and its harmonics were deleted using state-of-the-art methods, i.e. (1) a band-stop filter, (2) a band-pass filter, and (3) spectral linear interpolation. The same filtering methods were applied on the sEMG during the no-vibration trial. The linear interpolation method showed the highest intraclass correlation coefficients (no vibration: 0.999, WBV: 0.757-0.979) with the comparison measure (unfiltered sEMG during the no-vibration trial), followed by the band-stop filter (no vibration: 0.929-0.975, WBV: 0.661-0.938). While both methods introduced a systematic bias (P interpolation method and the band-stop filter was comparable. The band-pass filter was in poor agreement with the other methods (ICC: 0.207-0.697), unless the sEMG(RMS) was corrected for the bias (ICC ⩾ 0.931, %LOA ⩽ 32.3). In conclusion, spectral linear interpolation or a band-stop filter centered at the vibration frequency and its multiple harmonics should be applied to delete the artifacts in the sEMG signals during WBV. With the use of a band-stop filter it is recommended to correct the sEMG(RMS) for the bias as this procedure improved its performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

Wireless sEMG System with a Microneedle-Based High-Density Electrode Array on a Flexible Substrate.

Science.gov (United States)

Kim, Minjae; Gu, Gangyong; Cha, Kyoung Je; Kim, Dong Sung; Chung, Wan Kyun

2017-12-30

Surface electromyography (sEMG) signals reflect muscle contraction and hence, can provide information regarding a user's movement intention. High-density sEMG systems have been proposed to measure muscle activity in small areas and to estimate complex motion using spatial patterns. However, conventional systems based on wet electrodes have several limitations. For example, the electrolyte enclosed in wet electrodes restricts spatial resolution, and these conventional bulky systems limit natural movements. In this paper, a microneedle-based high-density electrode array on a circuit integrated flexible substrate for sEMG is proposed. Microneedles allow for high spatial resolution without requiring conductive substances, and flexible substrates guarantee stable skin-electrode contact. Moreover, a compact signal processing system is integrated with the electrode array. Therefore, sEMG measurements are comfortable to the user and do not interfere with the movement. The system performance was demonstrated by testing its operation and estimating motion using a Gaussian mixture model-based, simplified 2D spatial pattern.

sEMG during Whole-Body Vibration Contains Motion Artifacts and Reflex Activity

Directory of Open Access Journals (Sweden)

Karin Lienhard

2015-01-01

Full Text Available The purpose of this study was to determine whether the excessive spikes observed in the surface electromyography (sEMG spectrum recorded during whole-body vibration (WBV exercises contain motion artifacts and/or reflex activity. The occurrence of motion artifacts was tested by electrical recordings of the patella. The involvement of reflex activity was investigated by analyzing the magnitude of the isolated spikes during changes in voluntary background muscle activity. Eighteen physically active volunteers performed static squats while the sEMG was measured of five lower limb muscles during vertical WBV using no load and an additional load of 33 kg. In order to record motion artifacts during WBV, a pair of electrodes was positioned on the patella with several layers of tape between skin and electrodes. Spectral analysis of the patella signal revealed recordings of motion artifacts as high peaks at the vibration frequency (fundamental and marginal peaks at the multiple harmonics were observed. For the sEMG recordings, the root mean square of the spikes increased with increasing additional loads (p < 0.05, and was significantly correlated to the sEMG signal without the spikes of the respective muscle (r range: 0.54 - 0.92, p < 0.05. This finding indicates that reflex activity might be contained in the isolated spikes, as identical behavior has been found for stretch reflex responses evoked during direct vibration. In conclusion, the spikes visible in the sEMG spectrum during WBV exercises contain motion artifacts and possibly reflex activity.

The effectiveness of FES-evoked EMG potentials to assess muscle force and fatigue in individuals with spinal cord injury.

Science.gov (United States)

Ibitoye, Morufu Olusola; Estigoni, Eduardo H; Hamzaid, Nur Azah; Wahab, Ahmad Khairi Abdul; Davis, Glen M

2014-07-14

The evoked electromyographic signal (eEMG) potential is the standard index used to monitor both electrical changes within the motor unit during muscular activity and the electrical patterns during evoked contraction. However, technical and physiological limitations often preclude the acquisition and analysis of the signal especially during functional electrical stimulation (FES)-evoked contractions. Hence, an accurate quantification of the relationship between the eEMG potential and FES-evoked muscle response remains elusive and continues to attract the attention of researchers due to its potential application in the fields of biomechanics, muscle physiology, and rehabilitation science. We conducted a systematic review to examine the effectiveness of eEMG potentials to assess muscle force and fatigue, particularly as a biofeedback descriptor of FES-evoked contractions in individuals with spinal cord injury. At the outset, 2867 citations were identified and, finally, fifty-nine trials met the inclusion criteria. Four hypotheses were proposed and evaluated to inform this review. The results showed that eEMG is effective at quantifying muscle force and fatigue during isometric contraction, but may not be effective during dynamic contractions including cycling and stepping. Positive correlation of up to r = 0.90 (p peak-to-peak amplitude of the eEMG and the decline in the force output during fatiguing isometric contractions has been reported. In the available prediction models, the performance index of the eEMG signal to estimate the generated muscle force ranged from 3.8% to 34% for 18 s to 70 s ahead of the actual muscle force generation. The strength and inherent limitations of the eEMG signal to assess muscle force and fatigue were evident from our findings with implications in clinical management of spinal cord injury (SCI) population.

The Effectiveness of FES-Evoked EMG Potentials to Assess Muscle Force and Fatigue in Individuals with Spinal Cord Injury

Directory of Open Access Journals (Sweden)

Morufu Olusola Ibitoye

2014-07-01

Full Text Available The evoked electromyographic signal (eEMG potential is the standard index used to monitor both electrical changes within the motor unit during muscular activity and the electrical patterns during evoked contraction. However, technical and physiological limitations often preclude the acquisition and analysis of the signal especially during functional electrical stimulation (FES-evoked contractions. Hence, an accurate quantification of the relationship between the eEMG potential and FES-evoked muscle response remains elusive and continues to attract the attention of researchers due to its potential application in the fields of biomechanics, muscle physiology, and rehabilitation science. We conducted a systematic review to examine the effectiveness of eEMG potentials to assess muscle force and fatigue, particularly as a biofeedback descriptor of FES-evoked contractions in individuals with spinal cord injury. At the outset, 2867 citations were identified and, finally, fifty-nine trials met the inclusion criteria. Four hypotheses were proposed and evaluated to inform this review. The results showed that eEMG is effective at quantifying muscle force and fatigue during isometric contraction, but may not be effective during dynamic contractions including cycling and stepping. Positive correlation of up to r = 0.90 (p < 0.05 between the decline in the peak-to-peak amplitude of the eEMG and the decline in the force output during fatiguing isometric contractions has been reported. In the available prediction models, the performance index of the eEMG signal to estimate the generated muscle force ranged from 3.8% to 34% for 18 s to 70 s ahead of the actual muscle force generation. The strength and inherent limitations of the eEMG signal to assess muscle force and fatigue were evident from our findings with implications in clinical management of spinal cord injury (SCI population.

Seafloor backscatter signal simulation and classification

Digital Repository Service at National Institute of Oceanography (India)

Mahale, V.; El Dine, W.G.; Chakraborty, B.

. In this model a smooth echo envelope is generated then mixed up with multiplicative and additive noise. Several such echo signals were simulated for three types of seafloor. An Artificial Neural Network based classification technique is conceived to classify...

Unsupervised classification of operator workload from brain signals

Science.gov (United States)

Schultze-Kraft, Matthias; Dähne, Sven; Gugler, Manfred; Curio, Gabriel; Blankertz, Benjamin

2016-06-01

Objective. In this study we aimed for the classification of operator workload as it is expected in many real-life workplace environments. We explored brain-signal based workload predictors that differ with respect to the level of label information required for training, including entirely unsupervised approaches. Approach. Subjects executed a task on a touch screen that required continuous effort of visual and motor processing with alternating difficulty. We first employed classical approaches for workload state classification that operate on the sensor space of EEG and compared those to the performance of three state-of-the-art spatial filtering methods: common spatial patterns (CSPs) analysis, which requires binary label information; source power co-modulation (SPoC) analysis, which uses the subjects’ error rate as a target function; and canonical SPoC (cSPoC) analysis, which solely makes use of cross-frequency power correlations induced by different states of workload and thus represents an unsupervised approach. Finally, we investigated the effects of fusing brain signals and peripheral physiological measures (PPMs) and examined the added value for improving classification performance. Main results. Mean classification accuracies of 94%, 92% and 82% were achieved with CSP, SPoC, cSPoC, respectively. These methods outperformed the approaches that did not use spatial filtering and they extracted physiologically plausible components. The performance of the unsupervised cSPoC is significantly increased by augmenting it with PPM features. Significance. Our analyses ensured that the signal sources used for classification were of cortical origin and not contaminated with artifacts. Our findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable.

EEG Signal Classification With Super-Dirichlet Mixture Model

DEFF Research Database (Denmark)

Ma, Zhanyu; Tan, Zheng-Hua; Prasad, Swati

2012-01-01

Classification of the Electroencephalogram (EEG) signal is a challengeable task in the brain-computer interface systems. The marginalized discrete wavelet transform (mDWT) coefficients extracted from the EEG signals have been frequently used in researches since they reveal features related...

An open and configurable embedded system for EMG pattern recognition implementation for artificial arms.

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Jun Liu; Fan Zhang; Huang, He Helen

2014-01-01

Pattern recognition (PR) based on electromyographic (EMG) signals has been developed for multifunctional artificial arms for decades. However, assessment of EMG PR control for daily prosthesis use is still limited. One of the major barriers is the lack of a portable and configurable embedded system to implement the EMG PR control. This paper aimed to design an open and configurable embedded system for EMG PR implementation so that researchers can easily modify and optimize the control algorithms upon our designed platform and test the EMG PR control outside of the lab environments. The open platform was built on an open source embedded Linux Operating System running a high-performance Gumstix board. Both the hardware and software system framework were openly designed. The system was highly flexible in terms of number of inputs/outputs and calibration interfaces used. Such flexibility enabled easy integration of our embedded system with different types of commercialized or prototypic artificial arms. Thus far, our system was portable for take-home use. Additionally, compared with previously reported embedded systems for EMG PR implementation, our system demonstrated improved processing efficiency and high system precision. Our long-term goals are (1) to develop a wearable and practical EMG PR-based control for multifunctional artificial arms, and (2) to quantify the benefits of EMG PR-based control over conventional myoelectric prosthesis control in a home setting.

Adaptive DCTNet for Audio Signal Classification

OpenAIRE

Xian, Yin; Pu, Yunchen; Gan, Zhe; Lu, Liang; Thompson, Andrew

2016-01-01

In this paper, we investigate DCTNet for audio signal classification. Its output feature is related to Cohen's class of time-frequency distributions. We introduce the use of adaptive DCTNet (A-DCTNet) for audio signals feature extraction. The A-DCTNet applies the idea of constant-Q transform, with its center frequencies of filterbanks geometrically spaced. The A-DCTNet is adaptive to different acoustic scales, and it can better capture low frequency acoustic information that is sensitive to h...

Electrotactile EMG feedback improves the control of prosthesis grasping force

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Schweisfurth, Meike A.; Markovic, Marko; Dosen, Strahinja; Teich, Florian; Graimann, Bernhard; Farina, Dario

2016-10-01

Objective. A drawback of active prostheses is that they detach the subject from the produced forces, thereby preventing direct mechanical feedback. This can be compensated by providing somatosensory feedback to the user through mechanical or electrical stimulation, which in turn may improve the utility, sense of embodiment, and thereby increase the acceptance rate. Approach. In this study, we compared a novel approach to closing the loop, namely EMG feedback (emgFB), to classic force feedback (forceFB), using electrotactile interface in a realistic task setup. Eleven intact-bodied subjects and one transradial amputee performed a routine grasping task while receiving emgFB or forceFB. The two feedback types were delivered through the same electrotactile interface, using a mixed spatial/frequency coding to transmit 8 discrete levels of the feedback variable. In emgFB, the stimulation transmitted the amplitude of the processed myoelectric signal generated by the subject (prosthesis input), and in forceFB the generated grasping force (prosthesis output). The task comprised 150 trials of routine grasping at six forces, randomly presented in blocks of five trials (same force). Interquartile range and changes in the absolute error (AE) distribution (magnitude and dispersion) with respect to the target level were used to assess precision and overall performance, respectively. Main results. Relative to forceFB, emgFB significantly improved the precision of myoelectric commands (min/max of the significant levels) for 23%/36% as well as the precision of force control for 12%/32%, in intact-bodied subjects. Also, the magnitude and dispersion of the AE distribution were reduced. The results were similar in the amputee, showing considerable improvements. Significance. Using emgFB, the subjects therefore decreased the uncertainty of the forward pathway. Since there is a correspondence between the EMG and force, where the former anticipates the latter, the emgFB allowed for

A frequency and pulse-width co-modulation strategy for transcutaneous neuromuscular electrical stimulation based on sEMG time-domain features

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Zhou, Yu-Xuan; Wang, Hai-Peng; Bao, Xue-Liang; Lü, Xiao-Ying; Wang, Zhi-Gong

2016-02-01

Objective. Surface electromyography (sEMG) is often used as a control signal in neuromuscular electrical stimulation (NMES) systems to enhance the voluntary control and proprioceptive sensory feedback of paralyzed patients. Most sEMG-controlled NMES systems use the envelope of the sEMG signal to modulate the stimulation intensity (current amplitude or pulse width) with a constant frequency. The aims of this study were to develop a strategy that co-modulates frequency and pulse width based on features of the sEMG signal and to investigate the torque-reproduction performance and the level of fatigue resistance achieved with our strategy. Approach. We examined the relationships between wrist torque and two stimulation parameters (frequency and pulse width) and between wrist torque and two sEMG time-domain features (mean absolute value (MAV) and number of slope sign changes (NSS)) in eight healthy volunteers. By using wrist torque as an intermediate variable, customized and generalized transfer functions were constructed to convert the two features of the sEMG signal into the two stimulation parameters, thereby establishing a MAV/NSS dual-coding (MNDC) algorithm. Wrist torque reproduction performance was assessed by comparing the torque generated by the algorithms with that originally recorded during voluntary contractions. Muscle fatigue was assessed by measuring the decline percentage of the peak torque and by comparing the torque time integral of the response to test stimulation trains before and after fatigue sessions. Main Results. The MNDC approach could produce a wrist torque that closely matched the voluntary wrist torque. In addition, a smaller decay in the wrist torque was observed after the MNDC-coded fatigue stimulation was applied than after stimulation using pulse-width modulation alone. Significance. Compared with pulse-width modulation stimulation strategies that are based on sEMG detection, the MNDC strategy is more effective for both voluntary muscle

Simultaneous and Continuous Estimation of Shoulder and Elbow Kinematics from Surface EMG Signals

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Qin Zhang

2017-05-01

Full Text Available In this paper, we present a simultaneous and continuous kinematics estimation method for multiple DoFs across shoulder and elbow joint. Although simultaneous and continuous kinematics estimation from surface electromyography (EMG is a feasible way to achieve natural and intuitive human-machine interaction, few works investigated multi-DoF estimation across the significant joints of upper limb, shoulder and elbow joints. This paper evaluates the feasibility to estimate 4-DoF kinematics at shoulder and elbow during coordinated arm movements. Considering the potential applications of this method in exoskeleton, prosthetics and other arm rehabilitation techniques, the estimation performance is presented with different muscle activity decomposition and learning strategies. Principle component analysis (PCA and independent component analysis (ICA are respectively employed for EMG mode decomposition with artificial neural network (ANN for learning the electromechanical association. Four joint angles across shoulder and elbow are simultaneously and continuously estimated from EMG in four coordinated arm movements. By using ICA (PCA and single ANN, the average estimation accuracy 91.12% (90.23% is obtained in 70-s intra-cross validation and 87.00% (86.30% is obtained in 2-min inter-cross validation. This result suggests it is feasible and effective to use ICA (PCA with single ANN for multi-joint kinematics estimation in variant application conditions.

[The effect of EMG level by EMG biofeedback with progressive muscle relaxation training on tension headache].

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Ro, U J; Kim, N C; Kim, H S

1990-08-01

The purpose of this study is to assess if EMG biofeedback training with progressive muscle relaxation training is effective in reducing the EMG level in patients with tension headaches. This study which lasted from 23 October to 30 December 1989, was conducted on 10 females who were diagnosed as patients with tension headaches and selected from among volunteers at C. University in Seoul. The process of the study was as follows: First, before the treatment, the baseline was measured for two weeks and the level of EMG was measured five times in five minutes. And then EMG biofeedback training was used for six weeks, 12 sessions in all, and progressive muscle relaxation was done at home by audio tape over eight weeks. Each session was composed of a 5-minute baseline, two 5-minute EMG biofeedback training periods and a 5-minute self-control stage. Each stage was followed by a five minute rest period. So each session took a total of 40 minutes. The EMG level was measured by EMG biofeedback (Autogenic-Cyborg: M 130 EMG module). The results were as follows: 1. The average age of the subjects was 44.1 years and the average history of headache was 10.6 years (range: 6 months-20 years). 2. The level of EMG was lowest between the third and the fourth week of the training except in Cases I and IV. 3. The patients began to show a nonconciliatory attitude at the first session of the fifth week of the training.

Surface EMG and intra-socket force measurement to control a prosthetic device

Science.gov (United States)

Sanford, Joe; Patterson, Rita; Popa, Dan

2015-06-01

Surface electromyography (SEMG) has been shown to be a robust and reliable interaction method allowing for basic control of powered prosthetic devices. Research has shown a marked decrease in EMG-classification efficiency throughout activities of daily life due to socket shift and movement and fatigue as well as changes in degree of fit of the socket throughout the subject's lifetime. Users with the most severe levels of amputation require the most complex devices with the greatest number of degrees of freedom. Controlling complex dexterous devices with limited available inputs requires the addition of sensing and interaction modalities. However, the larger the amputation severity, the fewer viable SEMG sites are available as control inputs. Previous work reported the use of intra-socket pressure, as measured during wrist flexion and extension, and has shown that it is possible to control a powered prosthetic device with pressure sensors. In this paper, we present data correlations of SEMG data with intra-socket pressure data. Surface EMG sensors and force sensors were housed within a simulated prosthetic cuff fit to a healthy-limbed subject. EMG and intra-socket force data was collected from inside the cuff as a subject performed pre-defined grip motions with their dominant hand. Data fusion algorithms were explored and allowed a subject to use both intra-socket pressure and SEMG data as control inputs for a powered prosthetic device. This additional input modality allows for an improvement in input classification as well as information regarding socket fit through out activities of daily life.

KNN BASED CLASSIFICATION OF DIGITAL MODULATED SIGNALS

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Sajjad Ahmed Ghauri

2016-11-01

Full Text Available Demodulation process without the knowledge of modulation scheme requires Automatic Modulation Classification (AMC. When receiver has limited information about received signal then AMC become essential process. AMC finds important place in the field many civil and military fields such as modern electronic warfare, interfering source recognition, frequency management, link adaptation etc. In this paper we explore the use of K-nearest neighbor (KNN for modulation classification with different distance measurement methods. Five modulation schemes are used for classification purpose which is Binary Phase Shift Keying (BPSK, Quadrature Phase Shift Keying (QPSK, Quadrature Amplitude Modulation (QAM, 16-QAM and 64-QAM. Higher order cummulants (HOC are used as an input feature set to the classifier. Simulation results shows that proposed classification method provides better results for the considered modulation formats.

Seizure classification in EEG signals utilizing Hilbert-Huang transform

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Abdulhay Enas W

2011-05-01

Full Text Available Abstract Background Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG signals. Method Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering. Results The t-test results in a P-value Conclusion An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.

Seizure classification in EEG signals utilizing Hilbert-Huang transform.

Science.gov (United States)

Oweis, Rami J; Abdulhay, Enas W

2011-05-24

Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals. Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering. The t-test results in a P-value with respect to its fast response and ease to use. An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.

Patterns of motor recruitment can be determined using surface EMG.

Science.gov (United States)

Wakeling, James M

2009-04-01

Previous studies have reported how different populations of motor units (MUs) can be recruited during dynamic and locomotor tasks. It was hypothesised that the higher-threshold units would contribute higher-frequency components to the sEMG spectra due to their faster conduction velocities, and thus recruitment patterns that increase the proportion of high-threshold units active would lead to higher-frequency elements in the sEMG spectra. This idea was tested by using a model of varying recruitment coupled to a three-layer volume conductor model to generate a series of sEMG signals. The recruitment varied from (A) orderly recruitment where the lowest-threshold MUs were initially activated and higher-threshold MUs were sequentially recruited as the contraction progressed, (B) a recurrent inhibition model that started with orderly recruitment, but as the higher-threshold units were activated they inhibited the lower-threshold MUs (C) nine models with intermediate properties that were graded between these two extremes. The sEMG was processed using wavelet analysis and the spectral properties quantified by their mean frequency, and an angle theta that was determined from the principal components of the spectra. Recruitment strategies that resulted in a greater proportion of faster MUs being active had a significantly lower theta and higher mean frequency.

EMG BioanalyzerBR para a análise de sinais eletromiográficos na deglutição EMG BioanalyzerBR for analyzing electromyographic signals when swallowing

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Paulo Feodrippe

2012-06-01

Full Text Available OBJETIVO: descrever as etapas de construção do EMG BioanalyzerBR (versão 1.0 e demonstrar a sua aplicabilidade na análise de parâmetros fornecidos pela eletromiografia de superfície (EMGs. MÉTODOS: trata-se de um estudo descritivo do software de análise desenvolvido para analisar parâmetros obtidos na eletromiografia de superfície de músculos envolvidos na deglutição. Este software foi escrito em um ambiente de desenvolvimento utilizado por pesquisadores do mundo todo, de fácil acessibilidade e programação: o SCILAB. RESULTADOS: esta ferramenta se mostrou eficaz para a análise e transferência de dados nos registros curtos, contendo em média 10s de duração, porém para registros mais longos com duração maior que 20s apresentou falhas que não prejudicaram o cálculo após algumas tentativas. CONCLUSÃO: apesar das dificuldades, O EMG BioanalyzerBR possibilitou a realização das marcações canal por canal e quantas marcações fossem necessárias de forma simultânea,e desta forma a tabulação dos dados ficou mais rápida e com margem de falhas humanas reduzidas, porém com necessidade de aprimoramentos para a versão 2.0.PURPOSE: to describe the construction phases of EMG BioanalyzerBR (version 1.0 and demonstrate its applicability in analyzing parameters provided by surface electromyography (EMG. METHOD: it is a descriptive analysis software developed in order to analyze the parameters obtained in surface electromyography of muscles involved in swallowing. This software was written in a development environment used by worldwide researchers, with easy accessibility and programming: Scilab. RESULTS: this tool has proved effective for analyzing transferring short data records, having on average 10 seconds duration, but for with longest periods above 20s there were some failures that did not harm the calculation after a few tries. CONCLUSION: despite the difficulties, EMG BioanalyzerBR fostered the development of channel

Signal processing for passive detection and classification of underwater acoustic signals

Science.gov (United States)

Chung, Kil Woo

2011-12-01

This dissertation examines signal processing for passive detection, classification and tracking of underwater acoustic signals for improving port security and the security of coastal and offshore operations. First, we consider the problem of passive acoustic detection of a diver in a shallow water environment. A frequency-domain multi-band matched-filter approach to swimmer detection is presented. The idea is to break the frequency contents of the hydrophone signals into multiple narrow frequency bands, followed by time averaged (about half of a second) energy calculation over each band. Then, spectra composed of such energy samples over the chosen frequency bands are correlated to form a decision variable. The frequency bands with highest Signal/Noise ratio are used for detection. The performance of the proposed approach is demonstrated for experimental data collected for a diver in the Hudson River. We also propose a new referenceless frequency-domain multi-band detector which, unlike other reference-based detectors, does not require a diver specific signature. Instead, our detector matches to a general feature of the diver spectrum in the high frequency range: the spectrum is roughly periodic in time and approximately flat when the diver exhales. The performance of the proposed approach is demonstrated by using experimental data collected from the Hudson River. Moreover, we present detection, classification and tracking of small vessel signals. Hydroacoustic sensors can be applied for the detection of noise generated by vessels, and this noise can be used for vessel detection, classification and tracking. This dissertation presents recent improvements aimed at the measurement and separation of ship DEMON (Detection of Envelope Modulation on Noise) acoustic signatures in busy harbor conditions. Ship signature measurements were conducted in the Hudson River and NY Harbor. The DEMON spectra demonstrated much better temporal stability compared with the full ship

A Discrete-Time Algorithm for Stiffness Extraction from sEMG and Its Application in Antidisturbance Teleoperation

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Peidong Liang

2016-01-01

Full Text Available We have developed a new discrete-time algorithm of stiffness extraction from muscle surface electromyography (sEMG collected from human operator’s arms and have applied it for antidisturbance control in robot teleoperation. The variation of arm stiffness is estimated from sEMG signals and transferred to a telerobot under variable impedance control to imitate human motor control behaviours, particularly for disturbance attenuation. In comparison to the estimation of stiffness from sEMG, the proposed algorithm is able to reduce the nonlinear residual error effect and to enhance robustness and to simplify stiffness calibration. In order to extract a smoothing stiffness enveloping from sEMG signals, two enveloping methods are employed in this paper, namely, fast linear enveloping based on low pass filtering and moving average and amplitude monocomponent and frequency modulating (AM-FM method. Both methods have been incorporated into the proposed stiffness variance estimation algorithm and are extensively tested. The test results show that stiffness variation extraction based on the two methods is sensitive and robust to attenuation disturbance. It could potentially be applied for teleoperation in the presence of hazardous surroundings or human robot physical cooperation scenarios.

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System.

Science.gov (United States)

de Moura, Karina de O A; Balbinot, Alexandre

2018-05-01

A few prosthetic control systems in the scientific literature obtain pattern recognition algorithms adapted to changes that occur in the myoelectric signal over time and, frequently, such systems are not natural and intuitive. These are some of the several challenges for myoelectric prostheses for everyday use. The concept of the virtual sensor, which has as its fundamental objective to estimate unavailable measures based on other available measures, is being used in other fields of research. The virtual sensor technique applied to surface electromyography can help to minimize these problems, typically related to the degradation of the myoelectric signal that usually leads to a decrease in the classification accuracy of the movements characterized by computational intelligent systems. This paper presents a virtual sensor in a new extensive fault-tolerant classification system to maintain the classification accuracy after the occurrence of the following contaminants: ECG interference, electrode displacement, movement artifacts, power line interference, and saturation. The Time-Varying Autoregressive Moving Average (TVARMA) and Time-Varying Kalman filter (TVK) models are compared to define the most robust model for the virtual sensor. Results of movement classification were presented comparing the usual classification techniques with the method of the degraded signal replacement and classifier retraining. The experimental results were evaluated for these five noise types in 16 surface electromyography (sEMG) channel degradation case studies. The proposed system without using classifier retraining techniques recovered of mean classification accuracy was of 4% to 38% for electrode displacement, movement artifacts, and saturation noise. The best mean classification considering all signal contaminants and channel combinations evaluated was the classification using the retraining method, replacing the degraded channel by the virtual sensor TVARMA model. This method

An EMG-Controlled Robotic Hand Exoskeleton for Bilateral Rehabilitation.

Science.gov (United States)

Leonardis, Daniele; Barsotti, Michele; Loconsole, Claudio; Solazzi, Massimiliano; Troncossi, Marco; Mazzotti, Claudio; Castelli, Vincenzo Parenti; Procopio, Caterina; Lamola, Giuseppe; Chisari, Carmelo; Bergamasco, Massimo; Frisoli, Antonio

2015-01-01

This paper presents a novel electromyography (EMG)-driven hand exoskeleton for bilateral rehabilitation of grasping in stroke. The developed hand exoskeleton was designed with two distinctive features: (a) kinematics with intrinsic adaptability to patient's hand size, and (b) free-palm and free-fingertip design, preserving the residual sensory perceptual capability of touch during assistance in grasping of real objects. In the envisaged bilateral training strategy, the patient's non paretic hand acted as guidance for the paretic hand in grasping tasks. Grasping force exerted by the non paretic hand was estimated in real-time from EMG signals, and then replicated as robotic assistance for the paretic hand by means of the hand-exoskeleton. Estimation of the grasping force through EMG allowed to perform rehabilitation exercises with any, non sensorized, graspable objects. This paper presents the system design, development, and experimental evaluation. Experiments were performed within a group of six healthy subjects and two chronic stroke patients, executing robotic-assisted grasping tasks. Results related to performance in estimation and modulation of the robotic assistance, and to the outcomes of the pilot rehabilitation sessions with stroke patients, positively support validity of the proposed approach for application in stroke rehabilitation.

A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition

Science.gov (United States)

Huang, Qi; Yang, Dapeng; Jiang, Li; Zhang, Huajie; Liu, Hong; Kotani, Kiyoshi

2017-01-01

Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle). PMID:28608824

A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG Pattern Recognition

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Qi Huang

2017-06-01

Full Text Available Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC, by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC. We compared PAC performance with incremental support vector classifier (ISVC and non-adapting SVC (NSVC in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05 and ISVC (13.38% ± 2.62%, p = 0.001, and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle.

Non-stationarity and power spectral shifts in EMG activity reflect motor unit recruitment in rat diaphragm muscle.

Science.gov (United States)

Seven, Yasin B; Mantilla, Carlos B; Zhan, Wen-Zhi; Sieck, Gary C

2013-01-15

We hypothesized that a shift in diaphragm muscle (DIAm) EMG power spectral density (PSD) to higher frequencies reflects recruitment of more fatigable fast-twitch motor units and motor unit recruitment is reflected by EMG non-stationarity. DIAm EMG was recorded in anesthetized rats during eupnea, hypoxia-hypercapnia (10% O(2)-5% CO(2)), airway occlusion, and sneezing (maximal DIAm force). Although power in all frequency bands increased progressively across motor behaviors, PSD centroid frequency increased only during sneezing (pmotor units were recruited during different motor behaviors. Motor units augmented their discharge frequencies progressively beyond the non-stationary period; yet, EMG signal became stationary. In conclusion, non-stationarity of DIAm EMG reflects the period of motor unit recruitment, while a shift in the PSD towards higher frequencies reflects recruitment of more fatigable fast-twitch motor units. Copyright © 2012 Elsevier B.V. All rights reserved.

Cortico-muscular coherence on artifact corrected EEG-EMG data recorded with a MRI scanner.

Science.gov (United States)

Muthuraman, M; Galka, A; Hong, V N; Heute, U; Deuschl, G; Raethjen, J

2013-01-01

Simultaneous recording of electroencephalogram (EEG) and electromyogram (EMG) with magnetic resonance imaging (MRI) provides great potential for studying human brain activity with high temporal and spatial resolution. But, due to the MRI, the recorded signals are contaminated with artifacts. The correction of these artifacts is important to use these signals for further spectral analysis. The coherence can reveal the cortical representation of peripheral muscle signal in particular motor tasks, e.g. finger movements. The artifact correction of these signals was done by two different algorithms the Brain vision analyzer (BVA) and the Matlab FMRIB plug-in for EEGLAB. The Welch periodogram method was used for estimating the cortico-muscular coherence. Our analysis revealed coherence with a frequency of 5Hz in the contralateral side of the brain. The entropy is estimated for the calculated coherence to get the distribution of coherence in the scalp. The significance of the paper is to identify the optimal algorithm to rectify the MR artifacts and as a first step to use both these signals EEG and EMG in conjunction with MRI for further studies.

Classification Identification of Acoustic Emission Signals from Underground Metal Mine Rock by ICIMF Classifier

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Hongyan Zuo

2014-01-01

Full Text Available To overcome the drawback that fuzzy classifier was sensitive to noises and outliers, Mamdani fuzzy classifier based on improved chaos immune algorithm was developed, in which bilateral Gaussian membership function parameters were set as constraint conditions and the indexes of fuzzy classification effectiveness and number of correct samples of fuzzy classification as the subgoal of fitness function. Moreover, Iris database was used for simulation experiment, classification, and recognition of acoustic emission signals and interference signals from stope wall rock of underground metal mines. The results showed that Mamdani fuzzy classifier based on improved chaos immune algorithm could effectively improve the prediction accuracy of classification of data sets with noises and outliers and the classification accuracy of acoustic emission signal and interference signal from stope wall rock of underground metal mines was 90.00%. It was obvious that the improved chaos immune Mamdani fuzzy (ICIMF classifier was useful for accurate diagnosis of acoustic emission signal and interference signal from stope wall rock of underground metal mines.

Review: Painless EMG in Children

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Mahmoud Mohammadi

2003-12-01

Full Text Available Thanks to new techniques in Pediatric Neurology , nowadays we are more able to detect and differentiate different diseases of the nerves and muscles in children . Although these techniques are sometimes more sensitive and specific than EMG in children, but EMG and NCV study has its specific role in pediatric neurology and this is because of more availability and feasibility of these tests in children . One of the main Limitations of EMG techniques especially in pediatric age group is the pain induced by the insertion of needle electrodes into muscle as well as electrical stimulations needed to do NCV and other studies. So, all the experts in the field are trying to find some methods to reduce the pain induced by this technique . I have tried to introduce some of these methods after a brief explanation about pediatric EMG technique.

Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees

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John Jairo Villarejo Mayor

2017-08-01

Full Text Available Abstract Introduction Intuitive prosthesis control is one of the most important challenges in order to reduce the user effort in learning how to use an artificial hand. This work presents the development of a novel method for pattern recognition of sEMG signals able to discriminate, in a very accurate way, dexterous hand and fingers movements using a reduced number of electrodes, which implies more confidence and usability for amputees. Methods The system was evaluated for ten forearm amputees and the results were compared with the performance of able-bodied subjects. Multiple sEMG features based on fractal analysis (detrended fluctuation analysis and Higuchi’s fractal dimension combined with traditional magnitude-based features were analyzed. Genetic algorithms and sequential forward selection were used to select the best set of features. Support vector machine (SVM, K-nearest neighbors (KNN and linear discriminant analysis (LDA were analyzed to classify individual finger flexion, hand gestures and different grasps using four electrodes, performing contractions in a natural way to accomplish these tasks. Statistical significance was computed for all the methods using different set of features, for both groups of subjects (able-bodied and amputees. Results The results showed average accuracy up to 99.2% for able-bodied subjects and 98.94% for amputees using SVM, followed very closely by KNN. However, KNN also produces a good performance, as it has a lower computational complexity, which implies an advantage for real-time applications. Conclusion The results show that the method proposed is promising for accurately controlling dexterous prosthetic hands, providing more functionality and better acceptance for amputees.

Linear methods for reducing EMG contamination in peripheral nerve motor decodes.

Science.gov (United States)

Kagan, Zachary B; Wendelken, Suzanne; Page, David M; Davis, Tyler; Hutchinson, Douglas T; Clark, Gregory A; Warren, David J

2016-08-01

Signals recorded from the peripheral nervous system (PNS) with high channel count penetrating microelectrode arrays, such as the Utah Slanted Electrode Array (USEA), often have electromyographic (EMG) signals contaminating the neural signal. This common-mode signal source may prevent single neural units from successfully being detected, thus hindering motor decode algorithms. Reducing this EMG contamination may lead to more accurate motor decode performance. A virtual reference (VR), created by a weighted linear combination of signals from a subset of all available channels, can be used to reduce this EMG contamination. Four methods of determining individual channel weights and six different methods of selecting subsets of channels were investigated (24 different VR types in total). The methods of determining individual channel weights were equal weighting, regression-based weighting, and two different proximity-based weightings. The subsets of channels were selected by a radius-based criteria, such that a channel was included if it was within a particular radius of inclusion from the target channel. These six radii of inclusion were 1.5, 2.9, 3.2, 5, 8.4, and 12.8 electrode-distances; the 12.8 electrode radius includes all USEA electrodes. We found that application of a VR improves the detectability of neural events via increasing the SNR, but we found no statistically meaningful difference amongst the VR types we examined. The computational complexity of implementation varies with respect to the method of determining channel weights and the number of channels in a subset, but does not correlate with VR performance. Hence, we examined the computational costs of calculating and applying the VR and based on these criteria, we recommend an equal weighting method of assigning weights with a 3.2 electrode-distance radius of inclusion. Further, we found empirically that application of the recommended VR will require less than 1 ms for 33.3 ms of data from one USEA.

Analysis of EMG temporal parameters from the tibialis anterior during hemiparetic gait

International Nuclear Information System (INIS)

Bonell, Claudia E; Cherniz, AnalIa S; Tabernig, Carolina B

2007-01-01

Functional electrical stimulation is a rehabilitation technique used to restore the motor muscular function by means of electrical stimulus commanded by a trigger signal under volitional control. In order to enhance the motor rehabilitation, a more convenient control signal may be provided by the same muscle that is being stimulated. For example, the tibialis anterior (TA) in the applications of foot drop correction could be used. This work presents the statistical analysis of the root mean square (RMS) and the absolute mean value (VMA) of the TA electromyogram (EMG) signal computed from different phases of the gait cycle related with increases/decreases stages of muscle activity. The EMG records of 40 strides of 2 subjects with hemiparesia were processed. The RMS and VMA parameters allow distinguishing the oscillation phase from the other analyzed intervals, but they present significant spreading of mean values. This led to conclude that it is possible to use these parameters to identify the start of TA muscle activity, but altogether with other parameter or sensor that would reduce the number of false positives

Analysis of EMG temporal parameters from the tibialis anterior during hemiparetic gait

Energy Technology Data Exchange (ETDEWEB)

Bonell, Claudia E; Cherniz, AnalIa S; Tabernig, Carolina B [Laboratorio de Ingenieria de Rehabilitacion e Investigaciones Neuromusculares y Sensoriales, Facultad de Ingenieria, UNER, Oro Verde (Argentina)

2007-11-15

Functional electrical stimulation is a rehabilitation technique used to restore the motor muscular function by means of electrical stimulus commanded by a trigger signal under volitional control. In order to enhance the motor rehabilitation, a more convenient control signal may be provided by the same muscle that is being stimulated. For example, the tibialis anterior (TA) in the applications of foot drop correction could be used. This work presents the statistical analysis of the root mean square (RMS) and the absolute mean value (VMA) of the TA electromyogram (EMG) signal computed from different phases of the gait cycle related with increases/decreases stages of muscle activity. The EMG records of 40 strides of 2 subjects with hemiparesia were processed. The RMS and VMA parameters allow distinguishing the oscillation phase from the other analyzed intervals, but they present significant spreading of mean values. This led to conclude that it is possible to use these parameters to identify the start of TA muscle activity, but altogether with other parameter or sensor that would reduce the number of false positives.

Classification of subsurface objects using singular values derived from signal frames

Science.gov (United States)

Chambers, David H; Paglieroni, David W

2014-05-06

The classification system represents a detected object with a feature vector derived from the return signals acquired by an array of N transceivers operating in multistatic mode. The classification system generates the feature vector by transforming the real-valued return signals into complex-valued spectra, using, for example, a Fast Fourier Transform. The classification system then generates a feature vector of singular values for each user-designated spectral sub-band by applying a singular value decomposition (SVD) to the N.times.N square complex-valued matrix formed from sub-band samples associated with all possible transmitter-receiver pairs. The resulting feature vector of singular values may be transformed into a feature vector of singular value likelihoods and then subjected to a multi-category linear or neural network classifier for object classification.

Classification of EEG signals using a genetic-based machine learning classifier.

Science.gov (United States)

Skinner, B T; Nguyen, H T; Liu, D K

2007-01-01

This paper investigates the efficacy of the genetic-based learning classifier system XCS, for the classification of noisy, artefact-inclusive human electroencephalogram (EEG) signals represented using large condition strings (108bits). EEG signals from three participants were recorded while they performed four mental tasks designed to elicit hemispheric responses. Autoregressive (AR) models and Fast Fourier Transform (FFT) methods were used to form feature vectors with which mental tasks can be discriminated. XCS achieved a maximum classification accuracy of 99.3% and a best average of 88.9%. The relative classification performance of XCS was then compared against four non-evolutionary classifier systems originating from different learning techniques. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices.

Detection and Classification of Whale Acoustic Signals

Science.gov (United States)

Xian, Yin

This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification. In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information. In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase signals, we can represent the whale calls by their polynomial phase coefficients. In this dissertation, we used the Weyl transform to capture chirp rate information, and used a two dimensional feature set to represent whale vocalizations globally. Experimental results showed that our Weyl feature set outperforms chirplet coefficients and MFCC (Mel Frequency Cepstral Coefficients) when applied to our collected data. Since whale vocalizations can be represented by polynomial phase coefficients, it is plausible that the signals lie on a manifold parameterized by these coefficients. We also studied the intrinsic structure of high dimensional whale data by exploiting its geometry. Experimental results showed that nonlinear mappings such as Laplacian Eigenmap and ISOMAP outperform linear mappings such as PCA and MDS, suggesting that the whale acoustic data is nonlinear. We also explored deep learning algorithms on whale acoustic data. We built each layer as convolutions with either a PCA filter bank (PCANet) or a DCT filter bank (DCTNet). With the DCT filter bank, each layer has different a time-frequency scale representation, and from this, one can extract different physical information. Experimental results showed that our PCANet and DCTNet achieve high classification rate on the whale

A novel application of deep learning for single-lead ECG classification.

Science.gov (United States)

Mathews, Sherin M; Kambhamettu, Chandra; Barner, Kenneth E

2018-06-04

Detecting and classifying cardiac arrhythmias is critical to the diagnosis of patients with cardiac abnormalities. In this paper, a novel approach based on deep learning methodology is proposed for the classification of single-lead electrocardiogram (ECG) signals. We demonstrate the application of the Restricted Boltzmann Machine (RBM) and deep belief networks (DBN) for ECG classification following detection of ventricular and supraventricular heartbeats using single-lead ECG. The effectiveness of this proposed algorithm is illustrated using real ECG signals from the widely-used MIT-BIH database. Simulation results demonstrate that with a suitable choice of parameters, RBM and DBN can achieve high average recognition accuracies of ventricular ectopic beats (93.63%) and of supraventricular ectopic beats (95.57%) at a low sampling rate of 114 Hz. Experimental results indicate that classifiers built into this deep learning-based framework achieved state-of-the art performance models at lower sampling rates and simple features when compared to traditional methods. Further, employing features extracted at a sampling rate of 114 Hz when combined with deep learning provided enough discriminatory power for the classification task. This performance is comparable to that of traditional methods and uses a much lower sampling rate and simpler features. Thus, our proposed deep neural network algorithm demonstrates that deep learning-based methods offer accurate ECG classification and could potentially be extended to other physiological signal classifications, such as those in arterial blood pressure (ABP), nerve conduction (EMG), and heart rate variability (HRV) studies. Copyright © 2018. Published by Elsevier Ltd.

Sleep stage classification with low complexity and low bit rate.

Science.gov (United States)

Virkkala, Jussi; Värri, Alpo; Hasan, Joel; Himanen, Sari-Leena; Müller, Kiti

2009-01-01

Standard sleep stage classification is based on visual analysis of central (usually also frontal and occipital) EEG, two-channel EOG, and submental EMG signals. The process is complex, using multiple electrodes, and is usually based on relatively high (200-500 Hz) sampling rates. Also at least 12 bit analog to digital conversion is recommended (with 16 bit storage) resulting in total bit rate of at least 12.8 kbit/s. This is not a problem for in-house laboratory sleep studies, but in the case of online wireless self-applicable ambulatory sleep studies, lower complexity and lower bit rates are preferred. In this study we further developed earlier single channel facial EMG/EOG/EEG-based automatic sleep stage classification. An algorithm with a simple decision tree separated 30 s epochs into wakefulness, SREM, S1/S2 and SWS using 18-45 Hz beta power and 0.5-6 Hz amplitude. Improvements included low complexity recursive digital filtering. We also evaluated the effects of a reduced sampling rate, reduced number of quantization steps and reduced dynamic range on the sleep data of 132 training and 131 testing subjects. With the studied algorithm, it was possible to reduce the sampling rate to 50 Hz (having a low pass filter at 90 Hz), and the dynamic range to 244 microV, with an 8 bit resolution resulting in a bit rate of 0.4 kbit/s. Facial electrodes and a low bit rate enables the use of smaller devices for sleep stage classification in home environments.

Estimation of continuous multi-DOF finger joint kinematics from surface EMG using a multi-output Gaussian Process.

Science.gov (United States)

Ngeo, Jimson; Tamei, Tomoya; Shibata, Tomohiro

2014-01-01

Surface electromyographic (EMG) signals have often been used in estimating upper and lower limb dynamics and kinematics for the purpose of controlling robotic devices such as robot prosthesis and finger exoskeletons. However, in estimating multiple and a high number of degrees-of-freedom (DOF) kinematics from EMG, output DOFs are usually estimated independently. In this study, we estimate finger joint kinematics from EMG signals using a multi-output convolved Gaussian Process (Multi-output Full GP) that considers dependencies between outputs. We show that estimation of finger joints from muscle activation inputs can be improved by using a regression model that considers inherent coupling or correlation within the hand and finger joints. We also provide a comparison of estimation performance between different regression methods, such as Artificial Neural Networks (ANN) which is used by many of the related studies. We show that using a multi-output GP gives improved estimation compared to multi-output ANN and even dedicated or independent regression models.

Controlling a human-computer interface system with a novel classification method that uses electrooculography signals.

Science.gov (United States)

Wu, Shang-Lin; Liao, Lun-De; Lu, Shao-Wei; Jiang, Wei-Ling; Chen, Shi-An; Lin, Chin-Teng

2013-08-01

Electrooculography (EOG) signals can be used to control human-computer interface (HCI) systems, if properly classified. The ability to measure and process these signals may help HCI users to overcome many of the physical limitations and inconveniences in daily life. However, there are currently no effective multidirectional classification methods for monitoring eye movements. Here, we describe a classification method used in a wireless EOG-based HCI device for detecting eye movements in eight directions. This device includes wireless EOG signal acquisition components, wet electrodes and an EOG signal classification algorithm. The EOG classification algorithm is based on extracting features from the electrical signals corresponding to eight directions of eye movement (up, down, left, right, up-left, down-left, up-right, and down-right) and blinking. The recognition and processing of these eight different features were achieved in real-life conditions, demonstrating that this device can reliably measure the features of EOG signals. This system and its classification procedure provide an effective method for identifying eye movements. Additionally, it may be applied to study eye functions in real-life conditions in the near future.

Electroencephalography Signal Grouping and Feature Classification Using Harmony Search for BCI

Directory of Open Access Journals (Sweden)

Tae-Ju Lee

2013-01-01

Full Text Available This paper presents a heuristic method for electroencephalography (EEG grouping and feature classification using harmony search (HS for improving the accuracy of the brain-computer interface (BCI system. EEG, a noninvasive BCI method, uses many electrodes on the scalp, and a large number of electrodes make the resulting analysis difficult. In addition, traditional EEG analysis cannot handle multiple stimuli. On the other hand, the classification method using the EEG signal has a low accuracy. To solve these problems, we use a heuristic approach to reduce the complexities in multichannel problems and classification. In this study, we build a group of stimuli using the HS algorithm. Then, the features from common spatial patterns are classified by the HS classifier. To confirm the proposed method, we perform experiments using 64-channel EEG equipment. The subjects are subjected to three kinds of stimuli: audio, visual, and motion. Each stimulus is applied alone or in combination with the others. The acquired signals are processed by the proposed method. The classification results in an accuracy of approximately 63%. We conclude that the heuristic approach using the HS algorithm on the BCI is beneficial for EEG signal analysis.

High-density EMG e-textile systems for the control of active prostheses

DEFF Research Database (Denmark)

Farina, Dario; Lorrain, Thomas; Negro, Francesco

2010-01-01

Myoelectric control of active prostheses requires electrode systems that are easy to apply for daily repositioning of the electrodes by the user. In this study we propose the use of Smart Fabric and Interactive Textile (SFIT) systems as an alternative solution for recording high-density EMG signa...... classified with linear discriminant analysis. The average classification accuracy for the nine tasks was 89.1 1.9 %. These results show that SFIT systems can be used as an effective way for muscle-machine interfacing....

A probablistic neural network classification system for signal and image processing

Energy Technology Data Exchange (ETDEWEB)

Bowman, B. [Lawrence Livermore National Lab., CA (United States)

1994-11-15

The Acoustical Heart Valve Analysis Package is a system for signal and image processing and classification. It is being developed in both Matlab and C, to provide an interactive, interpreted environment, and has been optimized for large scale matrix operations. It has been used successfully to classify acoustic signals from implanted prosthetic heart valves in human patients, and will be integrated into a commercial Heart Valve Screening Center. The system uses several standard signal processing algorithms, as well as supervised learning techniques using the probabilistic neural network (PNN). Although currently used for the acoustic heart valve application, the algorithms and modular design allow it to be used for other applications, as well. We will describe the signal classification system, and show results from a set of test valves.

Local Wavelet-Based Filtering of Electromyographic Signals to Eliminate the Electrocardiographic-Induced Artifacts in Patients with Spinal Cord Injury.

Science.gov (United States)

Nitzken, Matthew; Bajaj, Nihit; Aslan, Sevda; Gimel'farb, Georgy; El-Baz, Ayman; Ovechkin, Alexander

2013-07-18

Surface Electromyography (EMG) is a standard method used in clinical practice and research to assess motor function in order to help with the diagnosis of neuromuscular pathology in human and animal models. EMG recorded from trunk muscles involved in the activity of breathing can be used as a direct measure of respiratory motor function in patients with spinal cord injury (SCI) or other disorders associated with motor control deficits. However, EMG potentials recorded from these muscles are often contaminated with heart-induced electrocardiographic (ECG) signals. Elimination of these artifacts plays a critical role in the precise measure of the respiratory muscle electrical activity. This study was undertaken to find an optimal approach to eliminate the ECG artifacts from EMG recordings. Conventional global filtering can be used to decrease the ECG-induced artifact. However, this method can alter the EMG signal and changes physiologically relevant information. We hypothesize that, unlike global filtering, localized removal of ECG artifacts will not change the original EMG signals. We develop an approach to remove the ECG artifacts without altering the amplitude and frequency components of the EMG signal by using an externally recorded ECG signal as a mask to locate areas of the ECG spikes within EMG data. These segments containing ECG spikes were decomposed into 128 sub-wavelets by a custom-scaled Morlet Wavelet Transform. The ECG-related sub-wavelets at the ECG spike location were removed and a de-noised EMG signal was reconstructed. Validity of the proposed method was proven using mathematical simulated synthetic signals and EMG obtained from SCI patients. We compare the Root-mean Square Error and the Relative Change in Variance between this method, global, notch and adaptive filters. The results show that the localized wavelet-based filtering has the benefit of not introducing error in the native EMG signal and accurately removing ECG artifacts from EMG signals.

Hybrid soft computing systems for electromyographic signals analysis: a review.

Science.gov (United States)

Xie, Hong-Bo; Guo, Tianruo; Bai, Siwei; Dokos, Socrates

2014-02-03

Electromyographic (EMG) is a bio-signal collected on human skeletal muscle. Analysis of EMG signals has been widely used to detect human movement intent, control various human-machine interfaces, diagnose neuromuscular diseases, and model neuromusculoskeletal system. With the advances of artificial intelligence and soft computing, many sophisticated techniques have been proposed for such purpose. Hybrid soft computing system (HSCS), the integration of these different techniques, aims to further improve the effectiveness, efficiency, and accuracy of EMG analysis. This paper reviews and compares key combinations of neural network, support vector machine, fuzzy logic, evolutionary computing, and swarm intelligence for EMG analysis. Our suggestions on the possible future development of HSCS in EMG analysis are also given in terms of basic soft computing techniques, further combination of these techniques, and their other applications in EMG analysis.

Emotion of Physiological Signals Classification Based on TS Feature Selection

Institute of Scientific and Technical Information of China (English)

Wang Yujing; Mo Jianlin

2015-01-01

This paper propose a method of TS-MLP about emotion recognition of physiological signal.It can recognize emotion successfully by Tabu search which selects features of emotion’s physiological signals and multilayer perceptron that is used to classify emotion.Simulation shows that it has achieved good emotion classification performance.

Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization

Science.gov (United States)

Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun

2016-01-01

Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656

Continuous Estimation of Human Multi-Joint Angles From sEMG Using a State-Space Model.

Science.gov (United States)

Ding, Qichuan; Han, Jianda; Zhao, Xingang

2017-09-01

Due to the couplings among joint-relative muscles, it is a challenge to accurately estimate continuous multi-joint movements from multi-channel sEMG signals. Traditional approaches always build a nonlinear regression model, such as artificial neural network, to predict the multi-joint movement variables using sEMG as inputs. However, the redundant sEMG-data are always not distinguished; the prediction errors cannot be evaluated and corrected online as well. In this work, a correlation-based redundancy-segmentation method is proposed to segment the sEMG-vector including redundancy into irredundant and redundant subvectors. Then, a general state-space framework is developed to build the motion model by regarding the irredundant subvector as input and the redundant one as measurement output. With the built state-space motion model, a closed-loop prediction-correction algorithm, i.e., the unscented Kalman filter (UKF), can be employed to estimate the multi-joint angles from sEMG, where the redundant sEMG-data are used to reject model uncertainties. After having fully employed the redundancy, the proposed method can provide accurate and smooth estimation results. Comprehensive experiments are conducted on the multi-joint movements of the upper limb. The maximum RMSE of the estimations obtained by the proposed method is 0.16±0.03, which is significantly less than 0.25±0.06 and 0.27±0.07 (p < 0.05) obtained by common neural networks.

Evaluation of novel algorithm embedded in a wearable sEMG device for seizure detection

DEFF Research Database (Denmark)

Conradsen, Isa; Beniczky, Sandor; Wolf, Peter

2012-01-01

We implemented a modified version of a previously published algorithm for detection of generalized tonic-clonic seizures into a prototype wireless surface electromyography (sEMG) recording device. The method was modified to require minimum computational load, and two parameters were trained...... on prior sEMG data recorded with the device. Along with the normal sEMG recording, the device is able to set an alarm whenever the implemented algorithm detects a seizure. These alarms are annotated in the data file along with the signal. The device was tested at the Epilepsy Monitoring Unit (EMU......) at the Danish Epilepsy Center. Five patients were included in the study and two of them had generalized tonic-clonic seizures. All patients were monitored for 2–5 days. A double-blind study was made on the five patients. The overall result showed that the device detected four of seven seizures and had a false...

Seizure classification in EEG signals utilizing Hilbert-Huang transform

OpenAIRE

Oweis, Rami J; Abdulhay, Enas W

2011-01-01

Abstract Background Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals. Method Discrimination in this ...

Single Trial Classification of Evoked EEG Signals Due to RGB Colors

Directory of Open Access Journals (Sweden)

Eman Alharbi

2016-03-01

Full Text Available Recently, the impact of colors on the brain signals has become one of the leading researches in BCI systems. These researches are based on studying the brain behavior after color stimulus, and finding a way to classify its signals offline without considering the real time. Moving to the next step, we present a real time classification model (online for EEG signals evoked by RGB colors stimuli, which is not presented in previous studies. In this research, EEG signals were recorded from 7 subjects through BCI2000 toolbox. The Empirical Mode Decomposition (EMD technique was used at the signal analysis stage. Various feature extraction methods were investigated to find the best and reliable set, including Event-related spectral perturbations (ERSP, Target mean with Feast Fourier Transform (FFT, Wavelet Packet Decomposition (WPD, Auto Regressive model (AR and EMD residual. A new feature selection method was created based on the peak's time of EEG signal when red and blue colors stimuli are presented. The ERP image was used to find out the peak's time, which was around 300 ms for the red color and around 450 ms for the blue color. The classification was performed using the Support Vector Machine (SVM classifier, LIBSVM toolbox being used for that purpose. The EMD residual was found to be the most reliable method that gives the highest classification accuracy with an average of 88.5% and with an execution time of only 14 seconds.

Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

Science.gov (United States)

Smith, Aaron; Evans, Michael; Downey, Joseph

2017-01-01

National Aeronautics and Space Administration (NASA)'s future communication architecture is evaluating cognitive technologies and increased system intelligence. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. A significant amount of previous work has been done in the area of automatic signal classification for military and commercial applications. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase-shift keying (PSK), amplitude and phase-shift keying (APSK),and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite- Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.

Multiple Signal Classification for Gravitational Wave Burst Search

Science.gov (United States)

Cao, Junwei; He, Zhengqi

2013-01-01

This work is mainly focused on the application of the multiple signal classification (MUSIC) algorithm for gravitational wave burst search. This algorithm extracts important gravitational wave characteristics from signals coming from detectors with arbitrary position, orientation and noise covariance. In this paper, the MUSIC algorithm is described in detail along with the necessary adjustments required for gravitational wave burst search. The algorithm's performance is measured using simulated signals and noise. MUSIC is compared with the Q-transform for signal triggering and with Bayesian analysis for direction of arrival (DOA) estimation, using the Ω-pipeline. Experimental results show that MUSIC has a lower resolution but is faster. MUSIC is a promising tool for real-time gravitational wave search for multi-messenger astronomy.

Efficient feature for classification of eye movements using electrooculography signals

OpenAIRE

Phukpattaranont Pornchai; Aungsakul Siriwadee; Phinyomark Angkoon; Limsakul Chusak

2016-01-01

Electrooculography (EOG) signal is widely and successfully used to detect activities of human eye. The advantages of the EOG-based interface over other conventional interfaces have been presented in the last two decades; however, due to a lot of information in EOG signals, the extraction of useful features should be done before the classification task. In this study, an efficient feature extracted from two directional EOG signals: vertical and horizontal si...

Hybrid soft computing systems for electromyographic signals analysis: a review

Science.gov (United States)

2014-01-01

Electromyographic (EMG) is a bio-signal collected on human skeletal muscle. Analysis of EMG signals has been widely used to detect human movement intent, control various human-machine interfaces, diagnose neuromuscular diseases, and model neuromusculoskeletal system. With the advances of artificial intelligence and soft computing, many sophisticated techniques have been proposed for such purpose. Hybrid soft computing system (HSCS), the integration of these different techniques, aims to further improve the effectiveness, efficiency, and accuracy of EMG analysis. This paper reviews and compares key combinations of neural network, support vector machine, fuzzy logic, evolutionary computing, and swarm intelligence for EMG analysis. Our suggestions on the possible future development of HSCS in EMG analysis are also given in terms of basic soft computing techniques, further combination of these techniques, and their other applications in EMG analysis. PMID:24490979

Locomotion mode identification for lower limbs using neuromuscular and joint kinematic signals.

Science.gov (United States)

Afzal, Taimoor; White, Gannon; Wright, Andrew B; Iqbal, Kamran

2014-01-01

Recent development in lower limb prosthetics has seen an emergence of powered prosthesis that have the capability to operate in different locomotion modes. However, these devices cannot transition seamlessly between modes such as level walking, stair ascent and descent and up slope and down slope walking. They require some form of user input that defines the human intent. The purpose of this study was to develop a locomotion mode detection system and evaluate its performance for different sensor configurations and to study the effect of locomotion mode detection with and without electromyography (EMG) signals while using kinematic data from hip joint of non-dominant/impaired limb and an accelerometer. Data was collected from four able bodied subjects that completed two circuits that contained standing, level-walking, ramp ascent and descent and stair ascent and descent. By using only the kinematic data from the hip joint and accelerometer data the system was able to identify the transitions, stance and swing phases with similar performance as compared to using only EMG and accelerometer data. However, significant improvement in classification error was observed when EMG, kinematic and accelerometer data were used together to identify the locomotion modes. The higher recognition rates when using the kinematic data along with EMG shows that the joint kinematics could be beneficial in intent recognition systems of locomotion modes.

Efficient feature for classification of eye movements using electrooculography signals

Directory of Open Access Journals (Sweden)

Phukpattaranont Pornchai

2016-01-01

Full Text Available Electrooculography (EOG signal is widely and successfully used to detect activities of human eye. The advantages of the EOG-based interface over other conventional interfaces have been presented in the last two decades; however, due to a lot of information in EOG signals, the extraction of useful features should be done before the classification task. In this study, an efficient feature extracted from two directional EOG signals: vertical and horizontal signals has been presented and evaluated. There are the maximum peak and valley amplitude values, the maximum peak and valley position values, and slope, which are derived from both vertical and horizontal signals. In the experiments, EOG signals obtained from five healthy subjects with ten directional eye movements were employed: up, down, right, left, up-right, up-left, down-right down-left clockwise and counterclockwise. The mean feature values and their standard deviations have been reported. The difference between the mean values of the proposed feature from different eye movements can be clearly seen. Using the scatter plot, the differences in features can be also clearly observed. Results show that classification accuracy can approach 100% with a simple distinction feature rule. The proposed features can be useful for various advanced human-computer interface applications in future researches.

Human-machine interfaces based on EMG and EEG applied to robotic systems

Directory of Open Access Journals (Sweden)

Sarcinelli-Filho Mario

2008-03-01

Full Text Available Abstract Background Two different Human-Machine Interfaces (HMIs were developed, both based on electro-biological signals. One is based on the EMG signal and the other is based on the EEG signal. Two major features of such interfaces are their relatively simple data acquisition and processing systems, which need just a few hardware and software resources, so that they are, computationally and financially speaking, low cost solutions. Both interfaces were applied to robotic systems, and their performances are analyzed here. The EMG-based HMI was tested in a mobile robot, while the EEG-based HMI was tested in a mobile robot and a robotic manipulator as well. Results Experiments using the EMG-based HMI were carried out by eight individuals, who were asked to accomplish ten eye blinks with each eye, in order to test the eye blink detection algorithm. An average rightness rate of about 95% reached by individuals with the ability to blink both eyes allowed to conclude that the system could be used to command devices. Experiments with EEG consisted of inviting 25 people (some of them had suffered cases of meningitis and epilepsy to test the system. All of them managed to deal with the HMI in only one training session. Most of them learnt how to use such HMI in less than 15 minutes. The minimum and maximum training times observed were 3 and 50 minutes, respectively. Conclusion Such works are the initial parts of a system to help people with neuromotor diseases, including those with severe dysfunctions. The next steps are to convert a commercial wheelchair in an autonomous mobile vehicle; to implement the HMI onboard the autonomous wheelchair thus obtained to assist people with motor diseases, and to explore the potentiality of EEG signals, making the EEG-based HMI more robust and faster, aiming at using it to help individuals with severe motor dysfunctions.

A novel deep learning approach for classification of EEG motor imagery signals.

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Tabar, Yousef Rezaei; Halici, Ugur

2017-02-01

Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully in many recent studies to learn features and classify different types of data. However, the number of studies that employ these approaches on BCI applications is very limited. In this study we aim to use deep learning methods to improve classification performance of EEG motor imagery signals. In this study we investigate convolutional neural networks (CNN) and stacked autoencoders (SAE) to classify EEG Motor Imagery signals. A new form of input is introduced to combine time, frequency and location information extracted from EEG signal and it is used in CNN having one 1D convolutional and one max-pooling layers. We also proposed a new deep network by combining CNN and SAE. In this network, the features that are extracted in CNN are classified through the deep network SAE. The classification performance obtained by the proposed method on BCI competition IV dataset 2b in terms of kappa value is 0.547. Our approach yields 9% improvement over the winner algorithm of the competition. Our results show that deep learning methods provide better classification performance compared to other state of art approaches. These methods can be applied successfully to BCI systems where the amount of data is large due to daily recording.

Effective Low-Power Wearable Wireless Surface EMG Sensor Design Based on Analog-Compressed Sensing

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Mohammadreza Balouchestani

2014-12-01

Full Text Available Surface Electromyography (sEMG is a non-invasive measurement process that does not involve tools and instruments to break the skin or physically enter the body to investigate and evaluate the muscular activities produced by skeletal muscles. The main drawbacks of existing sEMG systems are: (1 they are not able to provide real-time monitoring; (2 they suffer from long processing time and low speed; (3 they are not effective for wireless healthcare systems because they consume huge power. In this work, we present an analog-based Compressed Sensing (CS architecture, which consists of three novel algorithms for design and implementation of wearable wireless sEMG bio-sensor. At the transmitter side, two new algorithms are presented in order to apply the analog-CS theory before Analog to Digital Converter (ADC. At the receiver side, a robust reconstruction algorithm based on a combination of ℓ1-ℓ1-optimization and Block Sparse Bayesian Learning (BSBL framework is presented to reconstruct the original bio-signals from the compressed bio-signals. The proposed architecture allows reducing the sampling rate to 25% of Nyquist Rate (NR. In addition, the proposed architecture reduces the power consumption to 40%, Percentage Residual Difference (PRD to 24%, Root Mean Squared Error (RMSE to 2%, and the computation time from 22 s to 9.01 s, which provide good background for establishing wearable wireless healthcare systems. The proposed architecture achieves robust performance in low Signal-to-Noise Ratio (SNR for the reconstruction process.

Experiments on Classification of Electroencephalography (EEG Signals in Imagination of Direction using Stacked Autoencoder

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Kenta Tomonaga

2017-08-01

Full Text Available This paper presents classification methods for electroencephalography (EEG signals in imagination of direction measured by a portable EEG headset. In the authorsr previous studies, principal component analysis extracted significant features from EEG signals to construct neural network classifiers. To improve the performance, the authors have implemented a Stacked Autoencoder (SAE for the classification. The SAE carries out feature extraction and classification in a form of multi-layered neural network. Experimental results showed that the SAE outperformed the previous classifiers.

Comparison of methods for removing electromagnetic noise from electromyographic signals.

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Defreitas, Jason M; Beck, Travis W; Stock, Matt S

2012-02-01

The purpose of this investigation was to compare three different methods of removing noise from monopolar electromyographic (EMG) signals: (a) electrical shielding with a Faraday cage, (b) denoising with a digital notch-filter and (c) applying a bipolar differentiation with another monopolar EMG signal. Ten men and ten women (mean age = 24.0 years) performed isometric muscle actions of the leg extensors at 10-100% of their maximal voluntary contraction on two separate occasions. One trial was performed inside a Faraday tent (a flexible Faraday cage made from conductive material), and the other was performed outside the Faraday tent. The EMG signals collected outside the Faraday tent were analyzed three separate ways: as a raw signal, as a bipolar signal, and as a signal digitally notch filtered to remove 60 Hz noise and its harmonics. The signal-to-noise ratios were greatest after notch-filtering (range: 3.0-33.8), and lowest for the bipolar arrangement (1.6-10.2). Linear slope coefficients for the EMG amplitude versus force relationship were also used to compare the methods of noise removal. The results showed that a bipolar arrangement had a significantly lower linear slope coefficient when compared to the three other conditions (raw, notch and tent). These results suggested that an appropriately filtered monopolar EMG signal can be useful in situations that require a large pick-up area. Furthermore, although it is helpful, a Faraday tent (or cage) is not required to achieve an appropriate signal-to-noise ratio, as long as the correct filters are applied.

Comparison of methods for removing electromagnetic noise from electromyographic signals

International Nuclear Information System (INIS)

DeFreitas, Jason M; Beck, Travis W; Stock, Matt S

2012-01-01

The purpose of this investigation was to compare three different methods of removing noise from monopolar electromyographic (EMG) signals: (a) electrical shielding with a Faraday cage, (b) denoising with a digital notch-filter and (c) applying a bipolar differentiation with another monopolar EMG signal. Ten men and ten women (mean age = 24.0 years) performed isometric muscle actions of the leg extensors at 10–100% of their maximal voluntary contraction on two separate occasions. One trial was performed inside a Faraday tent (a flexible Faraday cage made from conductive material), and the other was performed outside the Faraday tent. The EMG signals collected outside the Faraday tent were analyzed three separate ways: as a raw signal, as a bipolar signal, and as a signal digitally notch filtered to remove 60 Hz noise and its harmonics. The signal-to-noise ratios were greatest after notch-filtering (range: 3.0–33.8), and lowest for the bipolar arrangement (1.6–10.2). Linear slope coefficients for the EMG amplitude versus force relationship were also used to compare the methods of noise removal. The results showed that a bipolar arrangement had a significantly lower linear slope coefficient when compared to the three other conditions (raw, notch and tent). These results suggested that an appropriately filtered monopolar EMG signal can be useful in situations that require a large pick-up area. Furthermore, although it is helpful, a Faraday tent (or cage) is not required to achieve an appropriate signal-to-noise ratio, as long as the correct filters are applied. (paper)

Comparison of conventional filtering and independent component analysis for artifact reduction in simultaneous gastric EMG and magnetogastrography from porcines.

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Irimia, Andrei; Richards, William O; Bradshaw, L Alan

2009-11-01

In this study, we perform a comparative study of independent component analysis (ICA) and conventional filtering (CF) for the purpose of artifact reduction from simultaneous gastric EMG and magnetogastrography (MGG). EMG/MGG data were acquired from ten anesthetized pigs by obtaining simultaneous recordings using serosal electrodes (EMG) as well as with a superconducting quantum interference device biomagnetometer (MGG). The analysis of MGG waveforms using ICA and CF indicates that ICA is superior to the CF method in its ability to extract respiration and cardiac artifacts from MGG recordings. A signal frequency analysis of ICA- and CF-processed data was also undertaken using waterfall plots, and it was determined that the two methods produce qualitatively comparable results. Through the use of simultaneous EMG/MGG, we were able to demonstrate the accuracy and trustworthiness of our results by comparison and cross-validation within the framework of a porcine model.

Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands.

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Atzori, Manfredo; Cognolato, Matteo; Müller, Henning

2016-01-01

Natural control methods based on surface electromyography (sEMG) and pattern recognition are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many real life applications, and commercial prostheses are capable of offering natural control for only a few movements. In recent years deep learning revolutionized several fields of machine learning, including computer vision and speech recognition. Our objective is to test its methods for natural control of robotic hands via sEMG using a large number of intact subjects and amputees. We tested convolutional networks for the classification of an average of 50 hand movements in 67 intact subjects and 11 transradial amputees. The simple architecture of the neural network allowed to make several tests in order to evaluate the effect of pre-processing, layer architecture, data augmentation and optimization. The classification results are compared with a set of classical classification methods applied on the same datasets. The classification accuracy obtained with convolutional neural networks using the proposed architecture is higher than the average results obtained with the classical classification methods, but lower than the results obtained with the best reference methods in our tests. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods. They show that several factors (including pre-processing, the architecture of the net and the optimization parameters) can be fundamental for the analysis of sEMG data. Larger networks can achieve higher accuracy on computer vision and object recognition tasks. This fact suggests that it may be interesting to evaluate if larger networks can increase sEMG classification accuracy too.

Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands

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Atzori, Manfredo; Cognolato, Matteo; Müller, Henning

2016-01-01

Natural control methods based on surface electromyography (sEMG) and pattern recognition are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many real life applications, and commercial prostheses are capable of offering natural control for only a few movements. In recent years deep learning revolutionized several fields of machine learning, including computer vision and speech recognition. Our objective is to test its methods for natural control of robotic hands via sEMG using a large number of intact subjects and amputees. We tested convolutional networks for the classification of an average of 50 hand movements in 67 intact subjects and 11 transradial amputees. The simple architecture of the neural network allowed to make several tests in order to evaluate the effect of pre-processing, layer architecture, data augmentation and optimization. The classification results are compared with a set of classical classification methods applied on the same datasets. The classification accuracy obtained with convolutional neural networks using the proposed architecture is higher than the average results obtained with the classical classification methods, but lower than the results obtained with the best reference methods in our tests. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods. They show that several factors (including pre-processing, the architecture of the net and the optimization parameters) can be fundamental for the analysis of sEMG data. Larger networks can achieve higher accuracy on computer vision and object recognition tasks. This fact suggests that it may be interesting to evaluate if larger networks can increase sEMG classification accuracy too. PMID:27656140

Reliability of MUAP properties in multi-channel array EMG recordings of trapezius and SCM

NARCIS (Netherlands)

Kallenberg, L.A.C.; Preece, S.; Hermens, Hermanus J.

2007-01-01

Muscle activity can be assessed non-invasively by means of surface electrodes places at the skin overlyin a muscle. When multiy-channel array electrodes are used, it is possible to extract motor unit action potentials (MUAP's) from the EMG signals with a segmentation approach based on the Continuous

Multichannel noninvasive human-machine interface via stretchable µm thick sEMG patches for robot manipulation

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Zhou, Ying; Wang, Youhua; Liu, Runfeng; Xiao, Lin; Zhang, Qin; Huang, YongAn

2018-01-01

Epidermal electronics (e-skin) emerging in recent years offer the opportunity to noninvasively and wearably extract biosignals from human bodies. The conventional processes of e-skin based on standard microelectronic fabrication processes and a variety of transfer printing methods, nevertheless, unquestionably constrains the size of the devices, posing a serious challenge to collecting signals via skin, the largest organ in the human body. Herein we propose a multichannel noninvasive human-machine interface (HMI) using stretchable surface electromyography (sEMG) patches to realize a robot hand mimicking human gestures. Time-efficient processes are first developed to manufacture µm thick large-scale stretchable devices. With micron thickness, the stretchable µm thick sEMG patches show excellent conformability with human skin and consequently comparable electrical performance with conventional gel electrodes. Combined with the large-scale size, the multichannel noninvasive HMI via stretchable µm thick sEMG patches successfully manipulates the robot hand with eight different gestures, whose precision is as high as conventional gel electrodes array.

Measuring leg movements during sleep using accelerometry: comparison with EMG and piezo-electric scored events.

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Terrill, Philip I; Leong, Matthew; Barton, Katrina; Freakley, Craig; Downey, Carl; Vanniekerk, Mark; Jorgensen, Greg; Douglas, James

2013-01-01

Periodic Limb Movements during Sleep (PLMS) can cause significant disturbance to sleep, resulting in daytime sleepiness and reduced quality of life. In conventional clinical practice, PLMS are measured using overnight electromyogram (EMG) of the tibialis anterior muscle, although historically they have also been measured using piezo-electric gauges placed over the muscle. However, PLMS counts (PLM index) do not correlate well with clinical symptomology. In this study, we propose that because EMG and piezo derived signals measure muscle activation rather than actual movement, they may count events with no appreciable movement of the limb and therefore no contribution to sleep disturbance. The aim of this study is thus to determine the percentage of clinically scored limb movements which are not associated with movement of the great toe measured using accelerometry. 9 participants were studied simultaneously with an overnight diagnostic polysomnogram (including EMG and piezo instrumentation of the right leg) and high temporal resolution accelerometry of the right great toe. Limb movements were scored, and peak acceleration during each scored movement was quantified. Across the participant population, 54.9% (range: 26.7-76.3) and 39.0% (range: 4.8-69.6) of limb movements scored using piezo and EMG instrumentation respectively, were not associated with toe movement measured with accelerometry. If sleep disturbance is the consequence of the limb movements, these results may explain why conventional piezo or EMG derived PLMI is poorly correlated with clinical symptomology.

BioSig: the free and open source software library for biomedical signal processing.

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Vidaurre, Carmen; Sander, Tilmann H; Schlögl, Alois

2011-01-01

BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respiration signals is a very relevant element of the BioSig project. Specifically, BioSig provides solutions for data acquisition, artifact processing, quality control, feature extraction, classification, modeling, and data visualization, to name a few. In this paper, we highlight several methods to help students and researchers to work more efficiently with biomedical signals.

Neural Correlates of Facial Mimicry: Simultaneous Measurements of EMG and BOLD Responses during Perception of Dynamic Compared to Static Facial Expressions

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Rymarczyk, Krystyna; Żurawski, Łukasz; Jankowiak-Siuda, Kamila; Szatkowska, Iwona

2018-01-01

Facial mimicry (FM) is an automatic response to imitate the facial expressions of others. However, neural correlates of the phenomenon are as yet not well established. We investigated this issue using simultaneously recorded EMG and BOLD signals during perception of dynamic and static emotional facial expressions of happiness and anger. During display presentations, BOLD signals and zygomaticus major (ZM), corrugator supercilii (CS) and orbicularis oculi (OO) EMG responses were recorded simultaneously from 46 healthy individuals. Subjects reacted spontaneously to happy facial expressions with increased EMG activity in ZM and OO muscles and decreased CS activity, which was interpreted as FM. Facial muscle responses correlated with BOLD activity in regions associated with motor simulation of facial expressions [i.e., inferior frontal gyrus, a classical Mirror Neuron System (MNS)]. Further, we also found correlations for regions associated with emotional processing (i.e., insula, part of the extended MNS). It is concluded that FM involves both motor and emotional brain structures, especially during perception of natural emotional expressions. PMID:29467691

Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review.

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Uddin, M B; Chow, C M; Su, S W

2018-03-26

Sleep apnea (SA), a common sleep disorder, can significantly decrease the quality of life, and is closely associated with major health risks such as cardiovascular disease, sudden death, depression, and hypertension. The normal diagnostic process of SA using polysomnography is costly and time consuming. In addition, the accuracy of different classification methods to detect SA varies with the use of different physiological signals. If an effective, reliable, and accurate classification method is developed, then the diagnosis of SA and its associated treatment will be time-efficient and economical. This study aims to systematically review the literature and present an overview of classification methods to detect SA using respiratory and oximetry signals and address the automated detection approach. Sixty-two included studies revealed the application of single and multiple signals (respiratory and oximetry) for the diagnosis of SA. Both airflow and oxygen saturation signals alone were effective in detecting SA in the case of binary decision-making, whereas multiple signals were good for multi-class detection. In addition, some machine learning methods were superior to the other classification methods for SA detection using respiratory and oximetry signals. To deal with the respiratory and oximetry signals, a good choice of classification method as well as the consideration of associated factors would result in high accuracy in the detection of SA. An accurate classification method should provide a high detection rate with an automated (independent of human action) analysis of respiratory and oximetry signals. Future high-quality automated studies using large samples of data from multiple patient groups or record batches are recommended.

An internet-based wearable watch-over system for elderly and disabled utilizing EMG and accelerometer.

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Kishimoto, M; Yoshida, T; Hayasaka, T; Mori, D; Imai, Y; Matsuki, N; Ishikawa, T; Yamaguchi, T

2009-01-01

An effective way for preventing injuries and diseases among the elderly is to monitor their daily lives. In this regard, we propose the use of a "Hyper Hospital Network", which is an information support system for elderly people and patients. In the current study, we developed a wearable system for monitoring electromyography (EMG) and acceleration using the Hyper Hospital Network plan. The current system is an upgraded version of our previous system for gait analysis (Yoshida et al. [13], Telemedicine and e-Health 13 703-714), and lets us monitor decreases in exercise and the presence of a hemiplegic gait more accurately. To clarify the capabilities and reliability of the system, we performed three experimental evaluations: one to verify the performance of the wearable system, a second to detect a hemiplegic gait, and a third to monitor EMG and accelerations simultaneously. Our system successfully detected a lack of exercise by monitoring the iEMG in healthy volunteers. Moreover, by using EMG and acceleration signals simultaneously, the reliability of the Hampering Index (HI) for detecting hemiplegia walking was improved significantly. The present study provides useful knowledge for the development of a wearable computer designed to monitor the physical conditions of older persons and patients.

Underwater object classification using scattering transform of sonar signals

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Saito, Naoki; Weber, David S.

2017-08-01

In this paper, we apply the scattering transform (ST)-a nonlinear map based off of a convolutional neural network (CNN)-to classification of underwater objects using sonar signals. The ST formalizes the observation that the filters learned by a CNN have wavelet-like structure. We achieve effective binary classification both on a real dataset of Unexploded Ordinance (UXOs), as well as synthetically generated examples. We also explore the effects on the waveforms with respect to changes in the object domain (e.g., translation, rotation, and acoustic impedance, etc.), and examine the consequences coming from theoretical results for the scattering transform. We show that the scattering transform is capable of excellent classification on both the synthetic and real problems, thanks to having more quasi-invariance properties that are well-suited to translation and rotation of the object.

Wavelet transform and ANNs for detection and classification of power signal disturbances

International Nuclear Information System (INIS)

Memon, A.P.; Uqaili, M.A.; Memon, Z.A.

2012-01-01

This article proposes WT (Wavelet Transform) and an ANN (Artificial Neural Network) based approach for detection and classification of EPQDs (Electrical Power Quality Disturbances). A modified WT known as ST (Stockwell Transform) is suggested for feature extraction and PNN (probabilistic Neural Network) for pattern classification. The ST possesses outstanding time-frequency resolution characteristics and its phase correction techniques determine the phase of the WT to the zero time point The feature vectors for the input of PNN are extracted using ST technique and these obtained features are discrete, logical, and unaffected to noisy data of distorted signals. The data of the models required to develop the distorted EPQ (Electrical Power Quality) signals, is obtained within the ranges specified by IEEE 1159-1995 in its literatures. The features vectors including noisy time varying data during steady state or transient condition and extracted using the ST, are trained through PNN for pattern classification. Their simulation results demonstrate that the proposed methodology is successful and can classify EPQDs even under a noisy environment very efficiently with an average classification accuracy of 96%. (author)

Use of sEMG in identification of low level muscle activities: features based on ICA and fractal dimension.

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Naik, Ganesh R; Kumar, Dinesh K; Arjunan, Sridhar

2009-01-01

This paper has experimentally verified and compared features of sEMG (Surface Electromyogram) such as ICA (Independent Component Analysis) and Fractal Dimension (FD) for identification of low level forearm muscle activities. The fractal dimension was used as a feature as reported in the literature. The normalized feature values were used as training and testing vectors for an Artificial neural network (ANN), in order to reduce inter-experimental variations. The identification accuracy using FD of four channels sEMG was 58%, and increased to 96% when the signals are separated to their independent components using ICA.

Design of sEMG assembly to detect external anal sphincter activity: a proof of concept.

Science.gov (United States)

Shiraz, Arsam; Leaker, Brian; Mosse, Charles Alexander; Solomon, Eskinder; Craggs, Michael; Demosthenous, Andreas

2017-10-31

Conditional trans-rectal stimulation of the pudendal nerve could provide a viable solution to treat hyperreflexive bladder in spinal cord injury. A set threshold of the amplitude estimate of the external anal sphincter surface electromyography (sEMG) may be used as the trigger signal. The efficacy of such a device should be tested in a large scale clinical trial. As such, a probe should remain in situ for several hours while patients attend to their daily routine; the recording electrodes should be designed to be large enough to maintain good contact while observing design constraints. The objective of this study was to arrive at a design for intra-anal sEMG recording electrodes for the subsequent clinical trials while deriving the possible recording and processing parameters. Having in mind existing solutions and based on theoretical and anatomical considerations, a set of four multi-electrode probes were designed and developed. These were tested in a healthy subject and the measured sEMG traces were recorded and appropriately processed. It was shown that while comparatively large electrodes record sEMG traces that are not sufficiently correlated with the external anal sphincter contractions, smaller electrodes may not maintain a stable electrode tissue contact. It was shown that 3 mm wide and 1 cm long electrodes with 5 mm inter-electrode spacing, in agreement with Nyquist sampling, placed 1 cm from the orifice may intra-anally record a sEMG trace sufficiently correlated with external anal sphincter activity. The outcome of this study can be used in any biofeedback, treatment or diagnostic application where the activity of the external anal sphincter sEMG should be detected for an extended period of time.

Facial EMG responses to emotional expressions are related to emotion perception ability.

Science.gov (United States)

Künecke, Janina; Hildebrandt, Andrea; Recio, Guillermo; Sommer, Werner; Wilhelm, Oliver

2014-01-01

Although most people can identify facial expressions of emotions well, they still differ in this ability. According to embodied simulation theories understanding emotions of others is fostered by involuntarily mimicking the perceived expressions, causing a "reactivation" of the corresponding mental state. Some studies suggest automatic facial mimicry during expression viewing; however, findings on the relationship between mimicry and emotion perception abilities are equivocal. The present study investigated individual differences in emotion perception and its relationship to facial muscle responses - recorded with electromyogram (EMG)--in response to emotional facial expressions. N° = °269 participants completed multiple tasks measuring face and emotion perception. EMG recordings were taken from a subsample (N° = °110) in an independent emotion classification task of short videos displaying six emotions. Confirmatory factor analyses of the m. corrugator supercilii in response to angry, happy, sad, and neutral expressions showed that individual differences in corrugator activity can be separated into a general response to all faces and an emotion-related response. Structural equation modeling revealed a substantial relationship between the emotion-related response and emotion perception ability, providing evidence for the role of facial muscle activation in emotion perception from an individual differences perspective.

sEMG-based joint force control for an upper-limb power-assist exoskeleton robot.

Science.gov (United States)

Li, Zhijun; Wang, Baocheng; Sun, Fuchun; Yang, Chenguang; Xie, Qing; Zhang, Weidong

2014-05-01

This paper investigates two surface electromyogram (sEMG)-based control strategies developed for a power-assist exoskeleton arm. Different from most of the existing position control approaches, this paper develops force control methods to make the exoskeleton robot behave like humans in order to provide better assistance. The exoskeleton robot is directly attached to a user's body and activated by the sEMG signals of the user's muscles, which reflect the user's motion intention. In the first proposed control method, the forces of agonist and antagonist muscles pair are estimated, and their difference is used to produce the torque of the corresponding joints. In the second method, linear discriminant analysis-based classifiers are introduced as the indicator of the motion type of the joints. Then, the classifier's outputs together with the estimated force of corresponding active muscle determine the torque control signals. Different from the conventional approaches, one classifier is assigned to each joint, which decreases the training time and largely simplifies the recognition process. Finally, the extensive experiments are conducted to illustrate the effectiveness of the proposed approaches.

Ventilatory threshold during incremental running can be estimated using EMG shorts.

Science.gov (United States)

Tikkanen, Olli; Hu, Min; Vilavuo, Toivo; Tolvanen, Pekka; Cheng, Sulin; Finni, Taija

2012-04-01

The present study examined whether shorts with textile electromyographic (EMG) electrodes can be used to detect second ventilatory threshold (V(T2)) during incremental treadmill running. Thirteen recreationally active (REC) and eight endurance athletes were measured for EMG, heart rate, blood lactate and respiratory gases during VO(2max) test (3 min ramps, 1 km·h(-1) increments). V(T)(2), onset of blood lactate accumulation (OBLA) and EMG threshold (EMG(T)) were determined. In athletes, OBLA occurred at 56 ± 6 mL·kg(-1)·min(-1), V(T2) occurred at 59 ± 6 mL·kg(-1)·min(-1), and EMG(T) at 62 ± 6 mL·kg(-1)·min(-1) without significant differences between methods (analysis of variance: ANOVA). In REC participants, OBLA occurred at 40 ± 10 mL·kg(-1)·min(-1), V(T2) occurred at 43 ± 7 mL·kg(-1)·min(-1), and EMG(T) at 41 ± 9 mL·kg(-1)·min(-1) without significant differences between methods (ANOVA). For the entire group, correlation between EMG(T) and V(T2) was 0.86 (P < 0.001) and 0.84 (P < 0.001) between EMG(T) and OBLA. Limits of agreement between EMG(T) and V(T2) were narrower in athletes than in REC participants. Thus, it is concluded that estimation of V(T2) using EMG(T) in athletes is more valid than in REC participants. In practice, experienced runners could use online feedback from EMG garments to monitor whether their running intensity is near V(T2). © 2012 Institute of Physics and Engineering in Medicine

pySPACE-a signal processing and classification environment in Python.

Science.gov (United States)

Krell, Mario M; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Teiwes, Johannes; Metzen, Jan H; Kirchner, Elsa A; Kirchner, Frank

2013-01-01

In neuroscience large amounts of data are recorded to provide insights into cerebral information processing and function. The successful extraction of the relevant signals becomes more and more challenging due to increasing complexities in acquisition techniques and questions addressed. Here, automated signal processing and machine learning tools can help to process the data, e.g., to separate signal and noise. With the presented software pySPACE (http://pyspace.github.io/pyspace), signal processing algorithms can be compared and applied automatically on time series data, either with the aim of finding a suitable preprocessing, or of training supervised algorithms to classify the data. pySPACE originally has been built to process multi-sensor windowed time series data, like event-related potentials from the electroencephalogram (EEG). The software provides automated data handling, distributed processing, modular build-up of signal processing chains and tools for visualization and performance evaluation. Included in the software are various algorithms like temporal and spatial filters, feature generation and selection, classification algorithms, and evaluation schemes. Further, interfaces to other signal processing tools are provided and, since pySPACE is a modular framework, it can be extended with new algorithms according to individual needs. In the presented work, the structural hierarchies are described. It is illustrated how users and developers can interface the software and execute offline and online modes. Configuration of pySPACE is realized with the YAML format, so that programming skills are not mandatory for usage. The concept of pySPACE is to have one comprehensive tool that can be used to perform complete signal processing and classification tasks. It further allows to define own algorithms, or to integrate and use already existing libraries.

Correlations and coherence of monopolar EMG-currents of the medial gastrocnemius muscle in proximal and distal compartments

Directory of Open Access Journals (Sweden)

Vinzenz eVon Tscharner

2014-06-01

Full Text Available The penniform gastrocnemius muscle contains multiple heads in the proximal regions and the aponeuroses are attached to the Achilles tendon. The multiple head structure lead to the assumption that different regions of the muscle must be activated compartment wise. The purpose of this study was to compare the correlation and coherence of EMG-currents within and between proximal and distal compartments of the medial gastrocnemius muscle, which reflect underling synchronization of motor units. It was hypothesized and shown that phase-inverted signals represent a property that discriminates compartments. However, the phase-inverted and non-inverted signals showed values of correlations that were indicative for highly synchronized signals. The correlation increased with the complexity of the task and was higher for the calf-rising movement than while balancing in a tiptoe position. Because the muscle fibers do not span the whole length of the muscles one has to conclude that the MUs were synchronized by synchronizing the various motor nerves. This study shows that it is essential to measure monopolar signals and use non-isometric contractions to observe synchronization of the EMG-signals. One could speculate that compartmental differences can only be observed if more complex movements that generate rotational forces at the knee or ankle are used.

Ventilatory threshold during incremental running can be estimated using EMG shorts

International Nuclear Information System (INIS)

Tikkanen, Olli; Vilavuo, Toivo; Finni, Taija; Hu, Min; Cheng, Sulin; Tolvanen, Pekka

2012-01-01

The present study examined whether shorts with textile electromyographic (EMG) electrodes can be used to detect second ventilatory threshold (V T2 ) during incremental treadmill running. Thirteen recreationally active (REC) and eight endurance athletes were measured for EMG, heart rate, blood lactate and respiratory gases during VO 2max test (3 min ramps, 1 km ⋅ h −1 increments). V T2 , onset of blood lactate accumulation (OBLA) and EMG threshold (EMG T ) were determined. In athletes, OBLA occurred at 56 ± 6 mL ⋅ kg −1  ⋅ min −1 , V T2 occurred at 59 ± 6 mL ⋅ kg −1  ⋅ min −1 , and EMG T at 62 ± 6 mL ⋅ kg −1  ⋅ min −1 without significant differences between methods (analysis of variance: ANOVA). In REC participants, OBLA occurred at 40 ± 10 mL ⋅ kg −1  ⋅ min −1 , V T2 occurred at 43 ± 7 mL ⋅ kg −1  ⋅ min −1 , and EMG T at 41 ± 9 mL ⋅ kg −1  ⋅ min −1 without significant differences between methods (ANOVA). For the entire group, correlation between EMG T and V T2 was 0.86 (P < 0.001) and 0.84 (P < 0.001) between EMG T and OBLA. Limits of agreement between EMG T and V T2 were narrower in athletes than in REC participants. Thus, it is concluded that estimation of V T2 using EMG T in athletes is more valid than in REC participants. In practice, experienced runners could use online feedback from EMG garments to monitor whether their running intensity is near V T2 . (paper)

Feature Optimize and Classification of EEG Signals: Application to Lie Detection Using KPCA and ELM

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GAO Junfeng

2014-04-01

Full Text Available EEG signals had been widely used to detect liars recent years. To overcome the shortcomings of current signals processing, kernel principal component analysis (KPCA and extreme learning machine (ELM was combined to detect liars. We recorded the EEG signals at Pz from 30 randomly divided guilty and innocent subjects. Each five Probe responses were averaged within subject and then extracted wavelet features. KPCA was employed to select feature subset with deduced dimensions based on initial wavelet features, which was fed into ELM. To date, there is no perfect solution for the number of its hidden nodes (NHN. We used grid searching algorithm to select simultaneously the optimal values of the dimension of feature subset and NHN based on cross- validation method. The best classification mode was decided with the optimal searching values. Experimental results show that for EEG signals from the experiment of lie detection, KPCA_ELM has higher classification accuracy with faster training speed than other widely-used classification modes, which is especially suitable for online EEG signals processing system.

An enhanced data visualization method for diesel engine malfunction classification using multi-sensor signals.

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Li, Yiqing; Wang, Yu; Zi, Yanyang; Zhang, Mingquan

2015-10-21

The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine.

pySPACE - A Signal Processing and Classification Environment in Python

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Mario Michael Krell

2013-12-01

Full Text Available In neuroscience large amounts of data are recorded to provide insights into cerebral information processing and function. The successful extraction of the relevant signals becomes more and more challenging due to increasing complexities in acquisition techniques and questions addressed. Here, automated signal processing and machine learning tools can help to process the data, e.g., to separate signal and noise. With the presented software pySPACE (http://pyspace.github.io/pyspace, signal processing algorithms can be compared and applied automatically on time series data, either with the aim of finding a suitable preprocessing, or of training supervised algorithms to classify the data. pySPACE originally has been built to process multi-sensor windowed time series data, like event-related potentials from the electroencephalogram (EEG. The software provides automated data handling, distributed processing, modular build-up of signal processing chains and tools for visualization and performance evaluation. Included in the software are various algorithms like temporal and spatial filters, feature generation and selection, classification algorithms and evaluation schemes. Further, interfaces to other signal processing tools are provided and, since pySPACE is a modular framework, it can be extended with new algorithms according to individual needs. In the presented work, the structural hierarchies are described. It is illustrated how users and developers can interface the software and execute offline and online modes. Configuration of pySPACE is realized with the YAML format, so that programming skills are not mandatory for usage. The concept of pySPACE is to have one comprehensive tool that can be used to perform complete signal processing and classification tasks. It further allows to define own algorithms, or to integrate and use already existing libraries.

EMG (elektromyografie jako metoda pro sledování účinnosti sportovního tréninku Surface EMG as a method for following-up sports training efficiency

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Damian Miklavčič

2005-02-01

training related changes in muscle contractile properties. Eight nationally ranked junior tennis players participated in a six weeks training program designed to increase speed and explosiveness. Their physical characteristics were evaluated before and after the training period by: tennis-specific field tests, measuring isometric twitch contraction of the medial gastrocnemius muscle, and by monitoring the frequency spectrum of the EMG at 50% of the maximal voluntary contraction. All the players improved the results of tennis specific field tests after the training period, but only three players were recognized to increase contractile speed of the medial gastrocnemius muscle expressed by shorter twitch contraction times after the training period. The same three players exhibited higher characteristic frequency (defined as the mean frequency lying between the sixth and ninth decile of the spectral distribution function and a wider EMG amplitude spectrum after the training period. A good correlation was found between the number of the parameters of the isometric twitch contraction that were improved by more than 2% after the training period (NP and the ratio between characteristic frequency after the training period (fA and characteristic frequency before the training period (fB (fA/fB (p = 0.0065, as well as between NP and the slope of the linear approximation of the dependence between decile frequencies of the EMG signal after the training period (dAf and decile frequencies of the EMG signal before the training period (dBf (dAf = f(dBf (p = 0.0035. The correlation between the number of parameters of the isometric twitch contraction that were improved after the training period and the changes in characteristic parameters of EMG suggests the applicability of EMG for following-up sports training efficiency.

Towards Automatic Classification of Exoplanet-Transit-Like Signals: A Case Study on Kepler Mission Data

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Valizadegan, Hamed; Martin, Rodney; McCauliff, Sean D.; Jenkins, Jon Michael; Catanzarite, Joseph; Oza, Nikunj C.

2015-08-01

Building new catalogues of planetary candidates, astrophysical false alarms, and non-transiting phenomena is a challenging task that currently requires a reviewing team of astrophysicists and astronomers. These scientists need to examine more than 100 diagnostic metrics and associated graphics for each candidate exoplanet-transit-like signal to classify it into one of the three classes. Considering that the NASA Explorer Program's TESS mission and ESA's PLATO mission survey even a larger area of space, the classification of their transit-like signals is more time-consuming for human agents and a bottleneck to successfully construct the new catalogues in a timely manner. This encourages building automatic classification tools that can quickly and reliably classify the new signal data from these missions. The standard tool for building automatic classification systems is the supervised machine learning that requires a large set of highly accurate labeled examples in order to build an effective classifier. This requirement cannot be easily met for classifying transit-like signals because not only are existing labeled signals very limited, but also the current labels may not be reliable (because the labeling process is a subjective task). Our experiments with using different supervised classifiers to categorize transit-like signals verifies that the labeled signals are not rich enough to provide the classifier with enough power to generalize well beyond the observed cases (e.g. to unseen or test signals). That motivated us to utilize a new category of learning techniques, so-called semi-supervised learning, that combines the label information from the costly labeled signals, and distribution information from the cheaply available unlabeled signals in order to construct more effective classifiers. Our study on the Kepler Mission data shows that semi-supervised learning can significantly improve the result of multiple base classifiers (e.g. Support Vector Machines, Ada

Advanced biofeedback from surface electromyography signals using fuzzy system

DEFF Research Database (Denmark)

Samani, Afshin; Holtermann, Andreas; Søgaard, Karen

2010-01-01

The aims of this study were to develop a fuzzy inference-based biofeedback system and investigate its effects when inducing active (shoulder elevation) and passive (relax) pauses on the trapezius muscle electromyographic (EMG) activity during computer work. Surface EMG signals were recorded from...

A New Method to Detect Driver Fatigue Based on EMG and ECG Collected by Portable Non-Contact Sensors

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Lin Wang

2017-11-01

Full Text Available Recently, detection and prediction on driver fatigue have become interest of research worldwide. In the present work, a new method is built to effectively evaluate driver fatigue based on electromyography (EMG and electrocardiogram (ECG collected by portable real-time and non-contact sensors. First, under the non-disturbance condition for driver’s attention, mixed physiological signals (EMG, ECG and artefacts are collected by non-contact sensors located in a cushion on the driver’s seat. EMG and ECG are effectively separated by FastICA, and de-noised by empirical mode decomposition (EMD. Then, three physiological features, complexity of EMG, complexity of ECG, and sample entropy (SampEn of ECG, are extracted and analysed. Principal components are obtained by principal components analysis (PCA and are used as independent variables. Finally, a mathematical model of driver fatigue is built, and the accuracy of the model is up to 91%. Moreover, based on the questionnaire, the calculation results of model are consistent with real fatigue felt by the participants. Therefore, this model can effectively detect driver fatigue.

Intelligent feature selection techniques for pattern classification of Lamb wave signals

International Nuclear Information System (INIS)

Hinders, Mark K.; Miller, Corey A.

2014-01-01

Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crosshole tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it’s never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes “line up” in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy

Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas

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Chestek, Cynthia A.; Gilja, Vikash; Blabe, Christine H.; Foster, Brett L.; Shenoy, Krishna V.; Parvizi, Josef; Henderson, Jaimie M.

2013-04-01

Objective. Brain-machine interface systems translate recorded neural signals into command signals for assistive technology. In individuals with upper limb amputation or cervical spinal cord injury, the restoration of a useful hand grasp could significantly improve daily function. We sought to determine if electrocorticographic (ECoG) signals contain sufficient information to select among multiple hand postures for a prosthetic hand, orthotic, or functional electrical stimulation system.Approach. We recorded ECoG signals from subdural macro- and microelectrodes implanted in motor areas of three participants who were undergoing inpatient monitoring for diagnosis and treatment of intractable epilepsy. Participants performed five distinct isometric hand postures, as well as four distinct finger movements. Several control experiments were attempted in order to remove sensory information from the classification results. Online experiments were performed with two participants. Main results. Classification rates were 68%, 84% and 81% for correct identification of 5 isometric hand postures offline. Using 3 potential controls for removing sensory signals, error rates were approximately doubled on average (2.1×). A similar increase in errors (2.6×) was noted when the participant was asked to make simultaneous wrist movements along with the hand postures. In online experiments, fist versus rest was successfully classified on 97% of trials; the classification output drove a prosthetic hand. Online classification performance for a larger number of hand postures remained above chance, but substantially below offline performance. In addition, the long integration windows used would preclude the use of decoded signals for control of a BCI system. Significance. These results suggest that ECoG is a plausible source of command signals for prosthetic grasp selection. Overall, avenues remain for improvement through better electrode designs and placement, better participant training

Young, healthy subjects can reduce the activity of calf muscles when provided with EMG biofeedback in upright stance

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Taian M. Vieira

2016-04-01

Full Text Available Recent evidence suggests the minimisation of muscular effort rather than of the size of bodily sway may be the primary, nervous system goal when regulating the human, standing posture. Different programs have been proposed for balance training; none however has been focused on the activation of postural muscles during standing. In this study we investigated the possibility of minimising the activation of the calf muscles during standing through biofeedback. By providing subjects with an audio signal that varied in amplitude and frequency with the amplitude of surface electromyograms (EMG recorded from different regions of the gastrocnemius and soleus muscles, we expected them to be able to minimise the level of muscle activation during standing without increasing the excursion of the centre of pressure (CoP. CoP data and surface EMG from gastrocnemii, soleus and tibialis anterior muscles were obtained from ten healthy participants while standing at ease and while standing with EMG biofeedback. Four sensitivities were used to test subjects’ responsiveness to the EMG biofeedback. Compared with standing at ease, the two most sensitive feedback conditions induced a decrease in plantar flexor activity (~15%; P<0.05 and an increase in tibialis anterior EMG (~10%; P<0.05. Furthermore, CoP mean position significantly shifted backward (~30 mm. In contrast, the use of less sensitive EMG biofeedback resulted in a significant decrease in EMG activity of ankle plantar flexors with a marginal increase in TA activity compared with standing at ease. These changes were not accompanied by greater CoP displacements or significant changes in mean CoP position. Key results revealed subjects were able to keep standing stability while reducing the activity of gastrocnemius and soleus without loading their tibialis anterior muscle when standing with EMG biofeedback. These results may therefore posit the basis for the development of training protocols aimed at

Fuzzy approximate entropy analysis of chaotic and natural complex systems: detecting muscle fatigue using electromyography signals.

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Xie, Hong-Bo; Guo, Jing-Yi; Zheng, Yong-Ping

2010-04-01

In the present contribution, a complexity measure is proposed to assess surface electromyography (EMG) in the study of muscle fatigue during sustained, isometric muscle contractions. Approximate entropy (ApEn) is believed to provide quantitative information about the complexity of experimental data that is often corrupted with noise, short data length, and in many cases, has inherent dynamics that exhibit both deterministic and stochastic behaviors. We developed an improved ApEn measure, i.e., fuzzy approximate entropy (fApEn), which utilizes the fuzzy membership function to define the vectors' similarity. Tests were conducted on independent, identically distributed (i.i.d.) Gaussian and uniform noises, a chirp signal, MIX processes, Rossler equation, and Henon map. Compared with the standard ApEn, the fApEn showed better monotonicity, relative consistency, and more robustness to noise when characterizing signals with different complexities. Performance analysis on experimental EMG signals demonstrated that the fApEn significantly decreased during the development of muscle fatigue, which is a similar trend to that of the mean frequency (MNF) of the EMG signal, while the standard ApEn failed to detect this change. Moreover, fApEn of EMG demonstrated a better robustness to the length of the analysis window in comparison with the MNF of EMG. The results suggest that the fApEn of an EMG signal may potentially become a new reliable method for muscle fatigue assessment and be applicable to other short noisy physiological signal analysis.

EMG of the hip adductor muscles in six clinical examination tests.

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Lovell, Gregory A; Blanch, Peter D; Barnes, Christopher J

2012-08-01

To assess activation of muscles of hip adduction using EMG and force analysis during standard clinical tests, and compare athletes with and without a prior history of groin pain. Controlled laboratory study. 21 male athletes from an elite junior soccer program. Bilateral surface EMG recordings of the adductor magnus, adductor longus, gracilis and pectineus as well as a unilateral fine-wire EMG of the pectineus were made during isometric holds in six clinical examination tests. A load cell was used to measure force data. Test type was a significant factor in the EMG output for all four muscles (all muscles p stronger than Hips 45, Hips 90 and Side lay. BMI (body mass index) was a significant factor (p Muscle EMG varied significantly with clinical test position. Athletes with previous groin injury had a significant fall in some EMG outputs. Copyright © 2011 Elsevier Ltd. All rights reserved.

Feature Extraction for Track Section Status Classification Based on UGW Signals

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Lei Yuan

2018-04-01

Full Text Available Track status classification is essential for the stability and safety of railway operations nowadays, when railway networks are becoming more and more complex and broad. In this situation, monitoring systems are already a key element in applications dedicated to evaluating the status of a certain track section, often determining whether it is free or occupied by a train. Different technologies have already been involved in the design of monitoring systems, including ultrasonic guided waves (UGW. This work proposes the use of the UGW signals captured by a track monitoring system to extract the features that are relevant for determining the corresponding track section status. For that purpose, three features of UGW signals have been considered: the root mean square value, the energy, and the main frequency components. Experimental results successfully validated how these features can be used to classify the track section status into free, occupied and broken. Furthermore, spatial and temporal dependencies among these features were analysed in order to show how they can improve the final classification performance. Finally, a preliminary high-level classification system based on deep learning networks has been envisaged for future works.

Masticatory Muscle Sleep Background EMG Activity is Elevated in Myofascial TMD Patients

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Raphael, Karen G.; Janal, Malvin N.; Sirois, David A.; Dubrovsky, Boris; Wigren, Pia E.; Klausner, Jack J.; Krieger, Ana C.; Lavigne, Gilles J.

2013-01-01

Despite theoretical speculation and strong clinical belief, recent research using laboratory polysomnographic (PSG) recording has provided new evidence that frequency of sleep bruxism (SB) masseter muscle events, including grinding or clenching of the teeth during sleep, is not increased for women with chronic myofascial temporomandibular disorder (TMD). The current case-control study compares a large sample of women suffering from chronic myofascial TMD (n=124) with a demographically matched control group without TMD (n=46) on sleep background electromyography (EMG) during a laboratory PSG study. Background EMG activity was measured as EMG root mean square (RMS) from the right masseter muscle after lights out. Sleep background EMG activity was defined as EMG RMS remaining after activity attributable to SB, other orofacial activity, other oromotor activity and movement artifacts were removed. Results indicated that median background EMG during these non SB-event periods was significantly higher (pcases exceeding control activity. Moreover, for TMD cases, background EMG was positively associated and SB event-related EMG was negatively associated with pain intensity ratings (0–10 numerical scale) on post sleep waking. These data provide the foundation for a new focus on small, but persistent, elevations in sleep EMG activity over the course of the night as a mechanism of pain induction or maintenance. PMID:24237356

Detecting and Predicting Muscle Fatigue during Typing By SEMG Signal Processing and Artificial Neural Networks

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Elham Ghoochani

2011-03-01

Full Text Available Introduction: Repetitive strain injuries are one of the most prevalent problems in occupational diseases. Repetition, vibration and bad postures of the extremities are physical risk factors related to work that can cause chronic musculoskeletal disorders. Repetitive work on a computer with low level contraction requires the posture to be maintained for a long time, which can cause muscle fatigue. Muscle fatigue in shoulders and neck is one of the most prevalent problems reported with computer users especially during typing. Surface electromyography (SEMG signals are used for detecting muscle fatigue as a non-invasive method. Material and Methods: Nine healthy females volunteered for signal recoding during typing. EMG signals were recorded from the trapezius muscle, which is subjected to muscle fatigue during typing.  After signal analysis and feature extraction, detecting and predicting muscle fatigue was performed by using the MLP artificial neural network. Results: Recorded signals were analyzed in time and frequency domains for feature extraction. Results of classification showed that the MLP neural network can detect and predict muscle fatigue during typing with 80.79 % ± 1.04% accuracy. Conclusion: Intelligent classification and prediction of muscle fatigue can have many applications in human factors engineering (ergonomics, rehabilitation engineering and biofeedback equipment for mitigating the injuries of repetitive works.

Predict or classify: The deceptive role of time-locking in brain signal classification

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Rusconi, Marco; Valleriani, Angelo

2016-06-01

Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal.

Real-Time Subject-Independent Pattern Classification of Overt and Covert Movements from fNIRS Signals.

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Neethu Robinson

Full Text Available Recently, studies have reported the use of Near Infrared Spectroscopy (NIRS for developing Brain-Computer Interface (BCI by applying online pattern classification of brain states from subject-specific fNIRS signals. The purpose of the present study was to develop and test a real-time method for subject-specific and subject-independent classification of multi-channel fNIRS signals using support-vector machines (SVM, so as to determine its feasibility as an online neurofeedback system. Towards this goal, we used left versus right hand movement execution and movement imagery as study paradigms in a series of experiments. In the first two experiments, activations in the motor cortex during movement execution and movement imagery were used to develop subject-dependent models that obtained high classification accuracies thereby indicating the robustness of our classification method. In the third experiment, a generalized classifier-model was developed from the first two experimental data, which was then applied for subject-independent neurofeedback training. Application of this method in new participants showed mean classification accuracy of 63% for movement imagery tasks and 80% for movement execution tasks. These results, and their corresponding offline analysis reported in this study demonstrate that SVM based real-time subject-independent classification of fNIRS signals is feasible. This method has important applications in the field of hemodynamic BCIs, and neuro-rehabilitation where patients can be trained to learn spatio-temporal patterns of healthy brain activity.

Classification of Steps on Road Surface Using Acceleration Signals

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Junji Takahashi

2015-12-01

Full Text Available In order to reduce a road monitoring cost, we propose a system to monitor extensively road condition by cyclists with a smartphone. In this paper, we propose two methods towards road monitoring. First is to classify road signals to four road conditions. Second is to extract road signal from a smartphone's accelerometer in three positions: pants' side pocket, chest pocket and a bag in a front basket. In pants' side pocket, road signal is extracted by Independent Component Analysis. In chest pocket and bag in a front basket, road signal is extracted by selecting 1-axis affected from gravitational acceleration. In the experiment of the classification method, overall accuracy was 75%. The experimental results of the extraction methods with correlation coefficient showed the overall accuracy were more than 0.7 in pants' side pocket and chest pocket, the overall accuracy was less than 0.3 in bag in a front basket.

Facial EMG responses to emotional expressions are related to emotion perception ability.

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Janina Künecke

Full Text Available Although most people can identify facial expressions of emotions well, they still differ in this ability. According to embodied simulation theories understanding emotions of others is fostered by involuntarily mimicking the perceived expressions, causing a "reactivation" of the corresponding mental state. Some studies suggest automatic facial mimicry during expression viewing; however, findings on the relationship between mimicry and emotion perception abilities are equivocal. The present study investigated individual differences in emotion perception and its relationship to facial muscle responses - recorded with electromyogram (EMG--in response to emotional facial expressions. N° = °269 participants completed multiple tasks measuring face and emotion perception. EMG recordings were taken from a subsample (N° = °110 in an independent emotion classification task of short videos displaying six emotions. Confirmatory factor analyses of the m. corrugator supercilii in response to angry, happy, sad, and neutral expressions showed that individual differences in corrugator activity can be separated into a general response to all faces and an emotion-related response. Structural equation modeling revealed a substantial relationship between the emotion-related response and emotion perception ability, providing evidence for the role of facial muscle activation in emotion perception from an individual differences perspective.

Evaluating EMG Feature and Classifier Selection for Application to Partial-Hand Prosthesis Control

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Adenike A. Adewuyi

2016-10-01

Full Text Available Pattern recognition-based myoelectric control of upper limb prostheses has the potential to restore control of multiple degrees of freedom. Though this control method has been extensively studied in individuals with higher-level amputations, few studies have investigated its effectiveness for individuals with partial-hand amputations. Most partial-hand amputees retain a functional wrist and the ability of pattern recognition-based methods to correctly classify hand motions from different wrist positions is not well studied. In this study, focusing on partial-hand amputees, we evaluate (1 the performance of non-linear and linear pattern recognition algorithms and (2 the performance of optimal EMG feature subsets for classification of four hand motion classes in different wrist positions for 16 non-amputees and 4 amputees. Our results show that linear discriminant analysis and linear and non-linear artificial neural networks perform significantly better than the quadratic discriminant analysis for both non-amputees and partial-hand amputees. For amputees, including information from multiple wrist positions significantly decreased error (p<0.001 but no further significant decrease in error occurred when more than 4, 2, or 3 positions were included for the extrinsic (p=0.07, intrinsic (p=0.06, or combined extrinsic and intrinsic muscle EMG (p=0.08, respectively. Finally, we found that a feature set determined by selecting optimal features from each channel outperformed the commonly used time domain (p<0.001 and time domain/autoregressive feature sets (p<0.01. This method can be used as a screening filter to select the features from each channel that provide the best classification of hand postures across different wrist positions.

SURFACE ELECTROMYOGRAPHY IN BIOMECHANICS: APPLICATIONS AND SIGNAL ANALYSIS ASPECTS

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DEAK GRAłIELA-FLAVIA

2009-12-01

Full Text Available Surface electromyography (SEMG is a technique for detecting and recording the electrical activity of the muscles using surface electrodes. The EMG signal is used in biomechanics mainly as an indicator of the initiation of muscle activation, as an indicator of the force produced by a contracting muscle, and as an index ofthe fatigue occurring within a muscle. EMG, used as a method of investigation, can tell us if the muscle is active or not, if the muscle is more or less active, when it is on or off, how much active is it, and finally, if it fatigues.The purpose of this article is to discuss some specific EMG signal analysis aspects with emphasis on comparison type analysis and frequency fatigue analysis.

THE EFFECTIVENESS OF EMG BIOFEEDBACK ON HAND FUNCTION IN SUBJECTS WITH STROKE

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S. Sethana

2014-10-01

Full Text Available Introduction: Stroke is an event caused by the interruption of the blood supply to the brain, usually because a blood vessel bursts or blocked by a clot. Biofeedback can be defined as the technique of using equipment usually electronic to reveal to human beings about some of their internal physiological events normal and abnormal in form of auditory and visual signals. Method: The stroke patients diagnosed by neurologist were recruited from physiotherapy department and inpatients from neurology and general wards of SVIMS hospital, Tirupathi Andhra Pradesh. In the present study 30 subjects were randomly assigned to 15 experimental and 15 control groups. The subject was made to sit comfortably and the Surfaces electrodes were placed on Extensor carpi radialis, Extensor digitorum communis muscle belly and for 30minutes patient voluntarily contracts until signals displayed on screen for which visually and auditory cues are given. In control group placebo EMG where machine is turned away & has no cues. Both groups received CONVENTIONAL PHYSIOTHERAPY; for 30 minutes at a Frequency: 1 hour per day for 5days in a week, for 6weeks. Results: There was statistically significant (p<0.05 improvement in both variables from baseline to 6thweek in experimental group compared to control group. Conclusion: Our study demonstrates the potential benefits of EMG BF in improving hand function in subjects with stroke.

Classification of Ultrasonic NDE Signals Using the Expectation Maximization (EM) and Least Mean Square (LMS) Algorithms

International Nuclear Information System (INIS)

Kim, Dae Won

2005-01-01

Ultrasonic inspection methods are widely used for detecting flaws in materials. The signal analysis step plays a crucial part in the data interpretation process. A number of signal processing methods have been proposed to classify ultrasonic flaw signals. One of the more popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature spare. This paper describes an alternative approach which uses the least mean square (LMS) method and exportation maximization (EM) algorithm with the model based deconvolution which is employed for classifying nondestructive evaluation (NDE) signals from steam generator tubes in a nuclear power plant. The signals due to cracks and deposits are not significantly different. These signals must be discriminated to prevent from happening a huge disaster such as contamination of water or explosion. A model based deconvolution has been described to facilitate comparison of classification results. The method uses the space alternating generalized expectation maximiBation (SAGE) algorithm ill conjunction with the Newton-Raphson method which uses the Hessian parameter resulting in fast convergence to estimate the time of flight and the distance between the tube wall and the ultrasonic sensor. Results using these schemes for the classification of ultrasonic signals from cracks and deposits within steam generator tubes are presented and showed a reasonable performances

Chronic recording of hand prosthesis control signals via a regenerative peripheral nerve interface in a rhesus macaque

Science.gov (United States)

Irwin, Z. T.; Schroeder, K. E.; Vu, P. P.; Tat, D. M.; Bullard, A. J.; Woo, S. L.; Sando, I. C.; Urbanchek, M. G.; Cederna, P. S.; Chestek, C. A.

2016-08-01

Objective. Loss of even part of the upper limb is a devastating injury. In order to fully restore natural function when lacking sufficient residual musculature, it is necessary to record directly from peripheral nerves. However, current approaches must make trade-offs between signal quality and longevity which limit their clinical potential. To address this issue, we have developed the regenerative peripheral nerve interface (RPNI) and tested its use in non-human primates. Approach. The RPNI consists of a small, autologous partial muscle graft reinnervated by a transected peripheral nerve branch. After reinnervation, the graft acts as a bioamplifier for descending motor commands in the nerve, enabling long-term recording of high signal-to-noise ratio (SNR), functionally-specific electromyographic (EMG) signals. We implanted nine RPNIs on separate branches of the median and radial nerves in two rhesus macaques who were trained to perform cued finger movements. Main results. No adverse events were noted in either monkey, and we recorded normal EMG with high SNR (>8) from the RPNIs for up to 20 months post-implantation. Using RPNI signals recorded during the behavioral task, we were able to classify each monkey’s finger movements as flexion, extension, or rest with >96% accuracy. RPNI signals also enabled functional prosthetic control, allowing the monkeys to perform the same behavioral task equally well with either physical finger movements or RPNI-based movement classifications. Significance. The RPNI signal strength, stability, and longevity demonstrated here represents a promising method for controlling advanced prosthetic limbs and fully restoring natural movement.

Humeral external rotation handling by using the Bobath concept approach affects trunk extensor muscles electromyography in children with cerebral palsy.

Science.gov (United States)

Grazziotin Dos Santos, C; Pagnussat, Aline S; Simon, A S; Py, Rodrigo; Pinho, Alexandre Severo do; Wagner, Mário B

2014-10-20

This study aimed to investigate the electromyographic activity of cervical and trunk extensors muscles in children with cerebral palsy during two handlings according to the Bobath concept. A crossover trial involving 40 spastic diplegic children was conducted. Electromyography (EMG) was used to measure muscular activity at sitting position (SP), during shoulder internal rotation (IR) and shoulder external rotation (ER) handlings, which were performed using the elbow joint as key point of control. Muscle recordings were performed at the fourth cervical (C4) and at the tenth thoracic (T10) vertebral levels. The Gross Motor Function Classification System (GMFCS) was used to assess whether muscle activity would vary according to different levels of severity. Humeral ER handling induced an increase on EMG signal of trunk extensor muscles at the C4 (P=0.007) and T10 (P<0.001) vertebral levels. No significant effects were observed between SP and humeral IR handling at C4 level; However at T10 region, humeral IR handling induced an increase of EMG signal (P=0.019). Humeral ER resulted in an increase of EMG signal at both levels, suggesting increase of extensor muscle activation. Furthermore, the humeral ER handling caused different responses on EMG signal at T10 vertebra level, according to the GMFCS classification (P=0.017). In summary, an increase of EMG signal was observed during ER handling in both evaluated levels, suggesting an increase of muscle activation. These results indicate that humeral ER handling can be used for diplegic CP children rehabilitation to facilitate cervical and trunk extensor muscles activity in a GMFCS level-dependent manner. Copyright © 2014 Elsevier Ltd. All rights reserved.

Low-back electromyography (EMG data-driven load classification for dynamic lifting tasks.

Directory of Open Access Journals (Sweden)

Deema Totah

Full Text Available Numerous devices have been designed to support the back during lifting tasks. To improve the utility of such devices, this research explores the use of preparatory muscle activity to classify muscle loading and initiate appropriate device activation. The goal of this study was to determine the earliest time window that enabled accurate load classification during a dynamic lifting task.Nine subjects performed thirty symmetrical lifts, split evenly across three weight conditions (no-weight, 10-lbs and 24-lbs, while low-back muscle activity data was collected. Seven descriptive statistics features were extracted from 100 ms windows of data. A multinomial logistic regression (MLR classifier was trained and tested, employing leave-one subject out cross-validation, to classify lifted load values. Dimensionality reduction was achieved through feature cross-correlation analysis and greedy feedforward selection. The time of full load support by the subject was defined as load-onset.Regions of highest average classification accuracy started at 200 ms before until 200 ms after load-onset with average accuracies ranging from 80% (±10% to 81% (±7%. The average recall for each class ranged from 69-92%.These inter-subject classification results indicate that preparatory muscle activity can be leveraged to identify the intent to lift a weight up to 100 ms prior to load-onset. The high accuracies shown indicate the potential to utilize intent classification for assistive device applications.Active assistive devices, e.g. exoskeletons, could prevent back injury by off-loading low-back muscles. Early intent classification allows more time for actuators to respond and integrate seamlessly with the user.

Electromyographic signal and force comparisons during maximal voluntary isometric contraction in water and on dry land.

Science.gov (United States)

Pinto, Stephanie Santana; Liedtke, Giane Veiga; Alberton, Cristine Lima; da Silva, Eduardo Marczwski; Cadore, Eduardo Lusa; Kruel, Luiz Fernando Martins

2010-11-01

This study was designed to compare surface electromyographic (sEMG) signal and force production during maximal voluntary isometric contractions (MVCs) in water and on dry land. The reproducibility of sEMG and isometric force measurements between water and dry land environments was also assessed. Nine women performed MVC for elbow flexion and extension, hip flexion, and extension against identical fixed resistance in both environments. The sEMG signal from biceps brachii, triceps brachii, rectus femoris, and biceps femoris was recorded with waterproof adhesives placed over each electrode. The sEMG and force production showed no significant difference between water and dry land, except for HEX (p = 0.035). In addition, intraclass correlation coefficient values were significant and ranged from moderate to high (0.66-0.96) for sEMG and force production between environments. These results showed that the environment did not influence the sEMG and force in MVC.

Intramuscular pressure and EMG relate during static contractions but dissociate with movement and fatigue

DEFF Research Database (Denmark)

Sjøgaard, Gisela; Jensen, Bente R.; Hargens, Allan R.

2004-01-01

Intramuscular pressure (IMP) and electromyography (EMG) mirror muscle force in the nonfatigued muscle during static contractions. The present study explores whether the constant IMP-EMG relationship with increased force may be extended to dynamic contractions and to fatigued muscle. IMP and EMG...... with speed of abduction. In the nonfatigued supraspinatus muscle, a linear relationship was found between IMP and EMG; in contrast, during fatigue and recovery, significant timewise changes of the IMP-to-EMG ratio occurred. The results indicate that IMP should be included along with EMG when mechanical load...... sharing between muscles is evaluated during dynamic and fatiguing contractions....

Noninvasive measurement of physiological signals on a modified home bathroom scale.

Science.gov (United States)

Inan, O T; Dookun Park; Giovangrandi, L; Kovacs, G T A

2012-08-01

A commercial bathroom scale with both handlebar and footpad electrodes was modified to enable measurement of four physiological signals: the ballistocardiogram (BCG), electrocardiogram (ECG), lower body impedance plethysmogram (IPG), and lower body electromyogram (EMG). The BCG, which describes the reaction of the body to cardiac ejection of blood, was measured using the strain gauges in the scale. The ECG was detected using handlebar electrodes with a two-electrode amplifier. For the lower body IPG, the two electrodes under the subject's toes were driven with an ac current stimulus, and the resulting differential voltage across the heels was measured and demodulated synchronously with the source. The voltage signal from the same two footpad electrodes under the heels was passed through a passive low-pass filter network into another amplifier, and the output was the lower body EMG signal. The signals were measured from nine healthy subjects, and the average signal-to-noise ratio (SNR) while the subjects were standing still was estimated for the four signals as follows: BCG, 7.6 dB; ECG, 15.8 dB; IPG, 10.7 dB. During periods of motion, the decrease in SNR for the BCG signal was found to be correlated to the increase in rms power for the lower body EMG (r = 0.89, p <; 0.01). The EMG could, thus, be used to flag noise-corrupted segments of the BCG, increasing the measurement robustness. This setup could be used for monitoring the cardiovascular health of patients at home.

Improving mental task classification by adding high frequency band information.

Science.gov (United States)

Zhang, Li; He, Wei; He, Chuanhong; Wang, Ping

2010-02-01

Features extracted from delta, theta, alpha, beta and gamma bands spanning low frequency range are commonly used to classify scalp-recorded electroencephalogram (EEG) for designing brain-computer interface (BCI) and higher frequencies are often neglected as noise. In this paper, we implemented an experimental validation to demonstrate that high frequency components could provide helpful information for improving the performance of the mental task based BCI. Electromyography (EMG) and electrooculography (EOG) artifacts were removed by using blind source separation (BSS) techniques. Frequency band powers and asymmetry ratios from the high frequency band (40-100 Hz) together with those from the lower frequency bands were used to represent EEG features. Finally, Fisher discriminant analysis (FDA) combining with Mahalanobis distance were used as the classifier. In this study, four types of classifications were performed using EEG signals recorded from four subjects during five mental tasks. We obtained significantly higher classification accuracy by adding the high frequency band features compared to using the low frequency bands alone, which demonstrated that the information in high frequency components from scalp-recorded EEG is valuable for the mental task based BCI.

Reliability of surface EMG measurements from the suprahyoid muscle complex

DEFF Research Database (Denmark)

Kothari, Mohit; Stubbs, Peter William; Pedersen, Asger Roer

2017-01-01

of using the suprahyoid muscle complex (SMC) using surface electromyography (sEMG) to assess changes to neural pathways by determining the reliability of measurements in healthy participants over days. Methods: Seventeen healthy participants were recruited. Measurements were performed twice with one week...... on stimulus type/intensity) had significantly different MEP values between day 1 and day 2 for single pulse and paired pulse TMS. A large stimulus artefact resulted in MEP responses that could not be assessed in four participants. Conclusions: The assessment of the SMC using sEMG following TMS was poorly...... reliable for ≈50% of participants. Although using sEMG to assess swallowing musculature function is easier to perform clinically and more comfortable to patients than invasive measures, as the measurement of muscle activity using TMS is unreliable, the use of sEMG for this muscle group is not recommended...

Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor

Science.gov (United States)

Rahman, Husna Abdul; Harun, Sulaiman Wadi; Arof, Hamzah; Irawati, Ninik; Musirin, Ismail; Ibrahim, Fatimah; Ahmad, Harith

2014-05-01

An enhanced dental cavity diameter measurement mechanism using an intensity-modulated fiber optic displacement sensor (FODS) scanning and imaging system, fuzzy logic as well as a single-layer perceptron (SLP) neural network, is presented. The SLP network was employed for the classification of the reflected signals, which were obtained from the surfaces of teeth samples and captured using FODS. Two features were used for the classification of the reflected signals with one of them being the output of a fuzzy logic. The test results showed that the combined fuzzy logic and SLP network methodology contributed to a 100% classification accuracy of the network. The high-classification accuracy significantly demonstrates the suitability of the proposed features and classification using SLP networks for classifying the reflected signals from teeth surfaces, enabling the sensor to accurately measure small diameters of tooth cavity of up to 0.6 mm. The method remains simple enough to allow its easy integration in existing dental restoration support systems.

Automatic sleep stage classification using two facial electrodes.

Science.gov (United States)

Virkkala, Jussi; Velin, Riitta; Himanen, Sari-Leena; Värri, Alpo; Müller, Kiti; Hasan, Joel

2008-01-01

Standard sleep stage classification is based on visual analysis of central EEG, EOG and EMG signals. Automatic analysis with a reduced number of sensors has been studied as an easy alternative to the standard. In this study, a single-channel electro-oculography (EOG) algorithm was developed for separation of wakefulness, SREM, light sleep (S1, S2) and slow wave sleep (S3, S4). The algorithm was developed and tested with 296 subjects. Additional validation was performed on 16 subjects using a low weight single-channel Alive Monitor. In the validation study, subjects attached the disposable EOG electrodes themselves at home. In separating the four stages total agreement (and Cohen's Kappa) in the training data set was 74% (0.59), in the testing data set 73% (0.59) and in the validation data set 74% (0.59). Self-applicable electro-oculography with only two facial electrodes was found to provide reasonable sleep stage information.

Identifying colon cancer risk modules with better classification performance based on human signaling network.

Science.gov (United States)

Qu, Xiaoli; Xie, Ruiqiang; Chen, Lina; Feng, Chenchen; Zhou, Yanyan; Li, Wan; Huang, Hao; Jia, Xu; Lv, Junjie; He, Yuehan; Du, Youwen; Li, Weiguo; Shi, Yuchen; He, Weiming

2014-10-01

Identifying differences between normal and tumor samples from a modular perspective may help to improve our understanding of the mechanisms responsible for colon cancer. Many cancer studies have shown that signaling transduction and biological pathways are disturbed in disease states, and expression profiles can distinguish variations in diseases. In this study, we integrated a weighted human signaling network and gene expression profiles to select risk modules associated with tumor conditions. Risk modules as classification features by our method had a better classification performance than other methods, and one risk module for colon cancer had a good classification performance for distinguishing between normal/tumor samples and between tumor stages. All genes in the module were annotated to the biological process of positive regulation of cell proliferation, and were highly associated with colon cancer. These results suggested that these genes might be the potential risk genes for colon cancer. Copyright © 2013. Published by Elsevier Inc.

Prediction of isometric motor tasks and effort levels based on high-density EMG in patients with incomplete spinal cord injury

Science.gov (United States)

Jordanić, Mislav; Rojas-Martínez, Mónica; Mañanas, Miguel Angel; Francesc Alonso, Joan

2016-08-01

Objective. The development of modern assistive and rehabilitation devices requires reliable and easy-to-use methods to extract neural information for control of devices. Group-specific pattern recognition identifiers are influenced by inter-subject variability. Based on high-density EMG (HD-EMG) maps, our research group has already shown that inter-subject muscle activation patterns exist in a population of healthy subjects. The aim of this paper is to analyze muscle activation patterns associated with four tasks (flexion/extension of the elbow, and supination/pronation of the forearm) at three different effort levels in a group of patients with incomplete Spinal Cord Injury (iSCI). Approach. Muscle activation patterns were evaluated by the automatic identification of these four isometric tasks along with the identification of levels of voluntary contractions. Two types of classifiers were considered in the identification: linear discriminant analysis and support vector machine. Main results. Results show that performance of classification increases when combining features extracted from intensity and spatial information of HD-EMG maps (accuracy = 97.5%). Moreover, when compared to a population with injuries at different levels, a lower variability between activation maps was obtained within a group of patients with similar injury suggesting stronger task-specific and effort-level-specific co-activation patterns, which enable better prediction results. Significance. Despite the challenge of identifying both the four tasks and the three effort levels in patients with iSCI, promising results were obtained which support the use of HD-EMG features for providing useful information regarding motion and force intention.

Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review

Science.gov (United States)

Hong, Keum-Shik; Khan, Muhammad Jawad

2017-01-01

In this article, non-invasive hybrid brain–computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain–computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided. PMID:28790910

Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review.

Science.gov (United States)

Hong, Keum-Shik; Khan, Muhammad Jawad

2017-01-01

In this article, non-invasive hybrid brain-computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain-computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.

Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review

Directory of Open Access Journals (Sweden)

Keum-Shik Hong

2017-07-01

Full Text Available In this article, non-invasive hybrid brain–computer interface (hBCI technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG, due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS, electromyography (EMG, electrooculography (EOG, and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain–computer interface (BCI accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.

Removal of EMG and ECG artifacts from EEG based on wavelet transform and ICA.

Science.gov (United States)

Zhou, Weidong; Gotman, Jean

2004-01-01

In this study, the methods of wavelet threshold de-noising and independent component analysis (ICA) are introduced. ICA is a novel signal processing technique based on high order statistics, and is used to separate independent components from measurements. The extended ICA algorithm does not need to calculate the higher order statistics, converges fast, and can be used to separate subGaussian and superGaussian sources. A pre-whitening procedure is performed to de-correlate the mixed signals before extracting sources. The experimental results indicate the electromyogram (EMG) and electrocardiograph (ECG) artifacts in electroencephalograph (EEG) can be removed by a combination of wavelet threshold de-noising and ICA.

Contemporary linkages between EMG, kinetics and stroke rehabilitation

OpenAIRE

Wolf, Steven L.; Butler, Andrew J.; Alberts, Jay L.; Kim, Min Wook

2005-01-01

EMG and kinetic measures have been primary tools in the study of movement and have provided the foundation for much of the work presented in this journal. Recently, novel ways of combining these tools have provided opportunities to examine elements of motor learning and brain plasticity. This presentation reviews the quantification of EMG within the context of transcranial magnetic stimulation. This vehicle permits acquisition of measures that are fundamental to examining prospects for cortic...

Portable EMG devices, Biofeedback and Contingent Electrical Stimulation applications in Bruxism

DEFF Research Database (Denmark)

Castrillon, Eduardo

Portable EMG devices, Biofeedback and Contingent Electrical Stimulation applications in Bruxism Eduardo Enrique, Castrillon Watanabe, DDS, MSc, PhD Section of Orofacial Pain and Jaw Function, Department of Dentistry, Aarhus University, Aarhus, Denmark; Scandinavian Center for Orofacial Neuroscience...... Summary: Bruxism is a parafunctional activity, which involves the masticatory muscles and probably it is as old as human mankind. Different methods such as portable EMG devices have been proposed to diagnose and understand the pathophysiology of bruxism. Biofeedback / contingent electrical stimulation...... characteristics make it complicated to assess bruxism using portable EMG devices. The possibility to assess bruxism like EMG activity on a portable device made it possible to use biofeedback and CES approaches in order to treat / manage bruxism. The available scientific information about CES effects on bruxism...

ECG Signal Processing, Classification and Interpretation A Comprehensive Framework of Computational Intelligence

CERN Document Server

Pedrycz, Witold

2012-01-01

Electrocardiogram (ECG) signals are among the most important sources of diagnostic information in healthcare so improvements in their analysis may also have telling consequences. Both the underlying signal technology and a burgeoning variety of algorithms and systems developments have proved successful targets for recent rapid advances in research. ECG Signal Processing, Classification and Interpretation shows how the various paradigms of Computational Intelligence, employed either singly or in combination, can produce an effective structure for obtaining often vital information from ECG signals. Neural networks do well at capturing the nonlinear nature of the signals, information granules realized as fuzzy sets help to confer interpretability on the data and evolutionary optimization may be critical in supporting the structural development of ECG classifiers and models of ECG signals. The contributors address concepts, methodology, algorithms, and case studies and applications exploiting the paradigm of Comp...

Analysis of High-Density Surface EMG and Finger Pressure in the Left Forearm of Violin Players: A Feasibility Study.

Science.gov (United States)

Cattarello, Paolo; Merletti, Roberto; Petracca, Francesco

2017-09-01

Wrist and finger flexor muscles of the left hand were evaluated using high-density surface EMG (HDsEMG) in 17 violin players. Pressure sensors also were mounted below the second string of the violin to evaluate, simultaneously, finger pressure. Electrode grid size was 110x70 mm (12x8 electrodes with interelectrode distance=10 mm and Ø=3 mm). The study objective was to observe the activation patterns of these muscles while the violinists sequentially played four notes--SI (B), DO# (C#), RE (D), MI (E)--at 2 bows/s (one bow up in 0.5 s and one down in 0.5 s) and 4 bows/s on the second string, while producing a constant (CONST) or ramp (RAMP) sound volume. HDsEMG images obtained while playing the notes were compared with those obtained during isometric radial or ulnar flexion of the wrist or fingers. Two image descriptors provided information on image differences. Results showed that the technique was reliable and provided reliable signals, and that recognizably different sEMG images could be associated with the four notes tested, despite the variability within and between subjects playing the same note. sEMG activity of the left hand muscles and pressure on the string in the RAMP task were strongly affected in some individuals by the sound volume (controlled by the right hand) and much less in other individuals. These findings question whether there is an individual or generally optimal way of pressing violin strings with the left hand. The answer to this question might substantially modify the teaching of string instruments.

An equilibrium-point model for fast, single-joint movement: I. Emergence of strategy-dependent EMG patterns.

Science.gov (United States)

Latash, M L; Gottlieb, G L

1991-09-01

We describe a model for the regulation of fast, single-joint movements, based on the equilibrium-point hypothesis. Limb movement follows constant rate shifts of independently regulated neuromuscular variables. The independently regulated variables are tentatively identified as thresholds of a length sensitive reflex for each of the participating muscles. We use the model to predict EMG patterns associated with changes in the conditions of movement execution, specifically, changes in movement times, velocities, amplitudes, and moments of limb inertia. The approach provides a theoretical neural framework for the dual-strategy hypothesis, which considers certain movements to be results of one of two basic, speed-sensitive or speed-insensitive strategies. This model is advanced as an alternative to pattern-imposing models based on explicit regulation of timing and amplitudes of signals that are explicitly manifest in the EMG patterns.

Influence of Joint Angle on EMG-Torque Model During Constant-Posture, Torque-Varying Contractions.

Science.gov (United States)

Liu, Pu; Liu, Lukai; Clancy, Edward A

2015-11-01

Relating the electromyogram (EMG) to joint torque is useful in various application areas, including prosthesis control, ergonomics and clinical biomechanics. Limited study has related EMG to torque across varied joint angles, particularly when subjects performed force-varying contractions or when optimized modeling methods were utilized. We related the biceps-triceps surface EMG of 22 subjects to elbow torque at six joint angles (spanning 60° to 135°) during constant-posture, torque-varying contractions. Three nonlinear EMG σ -torque models, advanced EMG amplitude (EMG σ ) estimation processors (i.e., whitened, multiple-channel) and the duration of data used to train models were investigated. When EMG-torque models were formed separately for each of the six distinct joint angles, a minimum "gold standard" error of 4.01±1.2% MVC(F90) resulted (i.e., error relative to maximum voluntary contraction at 90° flexion). This model structure, however, did not directly facilitate interpolation across angles. The best model which did so achieved a statistically equivalent error of 4.06±1.2% MVC(F90). Results demonstrated that advanced EMG σ processors lead to improved joint torque estimation as do longer model training durations.

Real-time classification of signals from three-component seismic sensors using neural nets

Science.gov (United States)

Bowman, B. C.; Dowla, F.

1992-05-01

Adaptive seismic data acquisition systems with capabilities of signal discrimination and event classification are important in treaty monitoring, proliferation, and earthquake early detection systems. Potential applications include monitoring underground chemical explosions, as well as other military, cultural, and natural activities where characteristics of signals change rapidly and without warning. In these applications, the ability to detect and interpret events rapidly without falling behind the influx of the data is critical. We developed a system for real-time data acquisition, analysis, learning, and classification of recorded events employing some of the latest technology in computer hardware, software, and artificial neural networks methods. The system is able to train dynamically, and updates its knowledge based on new data. The software is modular and hardware-independent; i.e., the front-end instrumentation is transparent to the analysis system. The software is designed to take advantage of the multiprocessing environment of the Unix operating system. The Unix System V shared memory and static RAM protocols for data access and the semaphore mechanism for interprocess communications were used. As the three-component sensor detects a seismic signal, it is displayed graphically on a color monitor using X11/Xlib graphics with interactive screening capabilities. For interesting events, the triaxial signal polarization is computed, a fast Fourier Transform (FFT) algorithm is applied, and the normalized power spectrum is transmitted to a backpropagation neural network for event classification. The system is currently capable of handling three data channels with a sampling rate of 500 Hz, which covers the bandwidth of most seismic events. The system has been tested in laboratory setting with artificial events generated in the vicinity of a three-component sensor.

Feature ranking and rank aggregation for automatic sleep stage classification: a comparative study.

Science.gov (United States)

Najdi, Shirin; Gharbali, Ali Abdollahi; Fonseca, José Manuel

2017-08-18

Nowadays, sleep quality is one of the most important measures of healthy life, especially considering the huge number of sleep-related disorders. Identifying sleep stages using polysomnographic (PSG) signals is the traditional way of assessing sleep quality. However, the manual process of sleep stage classification is time-consuming, subjective and costly. Therefore, in order to improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. Automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. Since classification accuracy is deeply affected by the extracted features, a poor feature vector will adversely affect the classifier and eventually lead to low classification accuracy. Therefore, special attention should be given to the feature extraction and selection process. In this paper the performance of seven feature selection methods, as well as two feature rank aggregation methods, were compared. Pz-Oz EEG, horizontal EOG and submental chin EMG recordings of 22 healthy males and females were used. A comprehensive feature set including 49 features was extracted from these recordings. The extracted features are among the most common and effective features used in sleep stage classification from temporal, spectral, entropy-based and nonlinear categories. The feature selection methods were evaluated and compared using three criteria: classification accuracy, stability, and similarity. Simulation results show that MRMR-MID achieves the highest classification performance while Fisher method provides the most stable ranking. In our simulations, the performance of the aggregation methods was in the average level, although they are known to generate more stable results and better accuracy. The Borda and RRA rank aggregation methods could not outperform significantly the conventional feature ranking methods. Among

An equilibrium-point model of electromyographic patterns during single-joint movements based on experimentally reconstructed control signals.

Science.gov (United States)

Latash, M L; Goodman, S R

1994-01-01

The purpose of this work has been to develop a model of electromyographic (EMG) patterns during single-joint movements based on a version of the equilibrium-point hypothesis, a method for experimental reconstruction of the joint compliant characteristics, the dual-strategy hypothesis, and a kinematic model of movement trajectory. EMG patterns are considered emergent properties of hypothetical control patterns that are equally affected by the control signals and peripheral feedback reflecting actual movement trajectory. A computer model generated the EMG patterns based on simulated movement kinematics and hypothetical control signals derived from the reconstructed joint compliant characteristics. The model predictions have been compared to published recordings of movement kinematics and EMG patterns in a variety of movement conditions, including movements over different distances, at different speeds, against different-known inertial loads, and in conditions of possible unexpected decrease in the inertial load. Changes in task parameters within the model led to simulated EMG patterns qualitatively similar to the experimentally recorded EMG patterns. The model's predictive power compares it favourably to the existing models of the EMG patterns. Copyright © 1994. Published by Elsevier Ltd.

Applied Chaos Level Test for Validation of Signal Conditions Underlying Optimal Performance of Voice Classification Methods

Science.gov (United States)

Liu, Boquan; Polce, Evan; Sprott, Julien C.; Jiang, Jack J.

2018-01-01

Purpose: The purpose of this study is to introduce a chaos level test to evaluate linear and nonlinear voice type classification method performances under varying signal chaos conditions without subjective impression. Study Design: Voice signals were constructed with differing degrees of noise to model signal chaos. Within each noise power, 100…

On the application of optimal wavelet filter banks for ECG signal classification

International Nuclear Information System (INIS)

Hadjiloucas, S; Jannah, N; Hwang, F; Galvão, R K H

2014-01-01

This paper discusses ECG signal classification after parametrizing the ECG waveforms in the wavelet domain. Signal decomposition using perfect reconstruction quadrature mirror filter banks can provide a very parsimonious representation of ECG signals. In the current work, the filter parameters are adjusted by a numerical optimization algorithm in order to minimize a cost function associated to the filter cut-off sharpness. The goal consists of achieving a better compromise between frequency selectivity and time resolution at each decomposition level than standard orthogonal filter banks such as those of the Daubechies and Coiflet families. Our aim is to optimally decompose the signals in the wavelet domain so that they can be subsequently used as inputs for training to a neural network classifier

EMG patterns during assisted walking in the exoskeleton

Science.gov (United States)

Sylos-Labini, Francesca; La Scaleia, Valentina; d'Avella, Andrea; Pisotta, Iolanda; Tamburella, Federica; Scivoletto, Giorgio; Molinari, Marco; Wang, Shiqian; Wang, Letian; van Asseldonk, Edwin; van der Kooij, Herman; Hoellinger, Thomas; Cheron, Guy; Thorsteinsson, Freygardur; Ilzkovitz, Michel; Gancet, Jeremi; Hauffe, Ralf; Zanov, Frank; Lacquaniti, Francesco; Ivanenko, Yuri P.

2014-01-01

Neuroprosthetic technology and robotic exoskeletons are being developed to facilitate stepping, reduce muscle efforts, and promote motor recovery. Nevertheless, the guidance forces of an exoskeleton may influence the sensory inputs, sensorimotor interactions and resulting muscle activity patterns during stepping. The aim of this study was to report the muscle activation patterns in a sample of intact and injured subjects while walking with a robotic exoskeleton and, in particular, to quantify the level of muscle activity during assisted gait. We recorded electromyographic (EMG) activity of different leg and arm muscles during overground walking in an exoskeleton in six healthy individuals and four spinal cord injury (SCI) participants. In SCI patients, EMG activity of the upper limb muscles was augmented while activation of leg muscles was typically small. Contrary to our expectations, however, in neurologically intact subjects, EMG activity of leg muscles was similar or even larger during exoskeleton-assisted walking compared to normal overground walking. In addition, significant variations in the EMG waveforms were found across different walking conditions. The most variable pattern was observed in the hamstring muscles. Overall, the results are consistent with a non-linear reorganization of the locomotor output when using the robotic stepping devices. The findings may contribute to our understanding of human-machine interactions and adaptation of locomotor activity patterns. PMID:24982628

EMG patterns during assisted walking in the exoskeleton

Directory of Open Access Journals (Sweden)

Francesca eSylos-Labini

2014-06-01

Full Text Available Neuroprosthetic technology and robotic exoskeletons are being developed to facilitate stepping, reduce muscle efforts and promote motor recovery. Nevertheless, the guidance forces of an exoskeleton may influence the sensory inputs, sensorimotor interactions and resulting muscle activity patterns during stepping. The aim of this study was to report the muscle activation patterns in a sample of intact and injured subjects while walking with a robotic exoskeleton and, in particular, to quantify the level of muscle activity during assisted gait. We recorded electromyographic (EMG activity of different leg and arm muscles during overground walking in an exoskeleton in six healthy individuals and four spinal cord injury (SCI participants. In SCI patients, EMG activity of the upper limb muscles was augmented while activation of leg muscles was typically small. Contrary to our expectations, however, in neurologically intact subjects, EMG activity of leg muscles was similar or even larger during exoskeleton-assisted walking compared to normal overground walking. In addition, significant variations in the EMG waveforms were found across different walking conditions. The most variable pattern was observed in the hamstring muscles. Overall, the results are consistent with a non-linear reorganization of the locomotor output when using the robotic stepping devices. The findings may contribute to our understanding of human-machine interactions and adaptation of locomotor activity patterns.

Effect of a jig on EMG activity in different orofacial pain conditions.

Science.gov (United States)

Bodere, Celine; Woda, Alain

2008-01-01

The bite stop (jig) is commonly used in clinical practice. It has been recommended as a simple means to routinely record or provide centric relation closure and, more recently, to reduce migraines and tension-type headaches. However, the reason for the jig effect has yet to be explained. This study tested the hypothesis that it works through a decrease in masticatory muscle activity. The effect of a jig placed on the maxillary anterior teeth was investigated by recording the electromyographic (EMG) activity of the superficial masseter and anterior temporal muscles at postural position and when swallowing on the jig. EMG recordings were obtained from 2 groups of pain patients (myofascial and neuropathic) and from 2 groups of pain-free patients (disc derangement and controls) unaware of the role of dental occlusion treatments. EMG activity in postural position was higher in pain groups than in pain-free groups. The jig strongly but temporarily decreased the postural EMG activity for masseter muscles in all groups except for the neuropathic group and for temporal muscles in the myofascial group. The EMG activity when swallowing with the jig was reduced in control, disc derangement, and myofascial groups; however, EMG "hyperactivity" in the neuropathic pain group seemed to be locked. The decrease of postural EMG activity, especially in the myofascial group, was short lasting and cannot be considered as evidence to support the hypothesis of a long-term muscle relaxation jig effect. However, the results may uphold certain short-term clinical approaches.

Design of human controlled 1 DOF right hand exoskeleton using electromyography signal

Science.gov (United States)

Azzam, M.; Wijaya, S. K.; Prawito

2017-07-01

Exoskeleton in general is a structure that is anatomically designed to be able to accommodate the physical movement of its user and provide additional strength. The use of EMG signal to control a 1 DOF right arm exoskeleton is evaluated in this research. This research aims to achieve optimum control using EMG signal. EMG signal is a variation of voltage that occurs when muscle contracts hence its strong correlation with the user's intention of movement. The RMS values of each EMG signal that originates from bicep and tricep muscle are calculated and processed to determine the direction and speed of rotation of a DC motor that actuates the exoskeleton. The RMS calculation is conducted at various array length that will theoretically affect its accuracy. The difference between those two RMS values is then calculated and interpreted as the intention of flexion or extension movement that will control the DC motor rotational direction. The absolute value of the RMS difference multiplied with a gain factor is used to regulate the duty cycle of a PWM signal that is used to control the rotational speed of the DC motor. To achieve the smallest settling time, array length and gain factor were varied. The test was conducted in two stages, static and dynamic tests. The test result shows a trend where the settling time decreases when array length is shortened and gain is increased. It shows that optimum control can be achieved by selecting the right array length and gain.

EMG biofeedback of the abductor pollicis brevis in piano performance.

Science.gov (United States)

Montes, R; Bedmar, M; Sol Martin, M

1993-06-01

The aim of the present study was to apply EMG biofeedback as an auxiliary to piano teaching techniques. We studied the changes in integrated electromyographic activity, using the abductor pollicis brevis functioning as an agonist during the teaching of identical selective movements of piano playing in two groups, one with EMG biofeedback and the other following traditional method of instruction. The analysis of variance revealed an increase in the peak amplitude and the relaxation rate values for the biofeedback group. These results have implications for the application of piano playing techniques and reveal EMG biofeedback as an aid in the teaching of thumb attack with the abductor pollicis brevis as agonist.

Development of Hand Grip Assistive Device Control System for Old People through Electromyography (EMG) Signal Acquisitions

OpenAIRE

Khamis Herman; Mohamaddan Shahrol; Komeda Takashi; Alias Aidil Azli; Tanjong Shirley Jonathan; Julai Norhuzaimin; Hashim Nurul ‘Izzati

2017-01-01

The hand grip assistive device is a glove to assist old people who suffer from hand weakness in their daily life activities. The device earlier control system only use simple on and off switch. This required old people to use both hand to activate the device. The new control system of the hand grip assistive device was developed to allow single hand operation for old people. New control system take advantages of electromyography (EMG) and flex sensor which was implemented to the device. It wa...

Fault Tolerant Neural Network for ECG Signal Classification Systems

Directory of Open Access Journals (Sweden)

MERAH, M.

2011-08-01

Full Text Available The aim of this paper is to apply a new robust hardware Artificial Neural Network (ANN for ECG classification systems. This ANN includes a penalization criterion which makes the performances in terms of robustness. Specifically, in this method, the ANN weights are normalized using the auto-prune method. Simulations performed on the MIT ? BIH ECG signals, have shown that significant robustness improvements are obtained regarding potential hardware artificial neuron failures. Moreover, we show that the proposed design achieves better generalization performances, compared to the standard back-propagation algorithm.

Classification Technique for Ultrasonic Weld Inspection Signals using a Neural Network based on 2-dimensional fourier Transform and Principle Component Analysis

International Nuclear Information System (INIS)

Kim, Jae Joon

2004-01-01

Neural network-based signal classification systems are increasingly used in the analysis of large volumes of data obtained in NDE applications. Ultrasonic inspection methods on the other hand are commonly used in the nondestructive evaluation of welds to detect flaws. An important characteristic of ultrasonic inspection is the ability to identify the type of discontinuity that gives rise to a peculiar signal. Standard techniques rely on differences in individual A-scans to classify the signals. This paper proposes an ultrasonic signal classification technique based on the information tying in the neighboring signals. The approach is based on a 2-dimensional Fourier transform and the principal component analysis to generate a reduced dimensional feature vector for classification. Results of applying the technique to data obtained from the inspection of actual steel welds are presented

Integrated Optimization of Long-Range Underwater Signal Detection, Feature Extraction, and Classification for Nuclear Treaty Monitoring

NARCIS (Netherlands)

Tuma, M.; Rorbech, V.; Prior, M.; Igel, C.

2016-01-01

We designed and jointly optimized an integrated signal processing chain for detection and classification of long-range passive-acoustic underwater signals recorded by the global geophysical monitoring network of the Comprehensive Nuclear-Test-Ban Treaty Organization. Starting at the level of raw

Cross Time-Frequency Analysis of Gastrocnemius Electromyographic Signals in Hypertensive and Nonhypertensive Subjects

Science.gov (United States)

Mitchell, Patrick; Krotish, Debra; Shin, Yong-June; Hirth, Victor

2010-12-01

The effects of hypertension are chronic and continuous; it affects gait, balance, and fall risk. Therefore, it is desirable to assess gait health across hypertensive and nonhypertensive subjects in order to prevent or reduce the risk of falls. Analysis of electromyography (EMG) signals can identify age related changes of neuromuscular activation due to various neuropathies and myopathies, but it is difficult to translate these medical changes to clinical diagnosis. To examine and compare geriatrics patients with these gait-altering diseases, we acquire EMG muscle activation signals, and by use of a timesynchronized mat capable of recording pressure information, we localize the EMG data to the gait cycle, ensuring identical comparison across subjects. Using time-frequency analysis on the EMG signal, in conjunction with several parameters obtained from the time-frequency analyses, we can determine the statistical discrepancy between diseases. We base these parameters on physiological manifestations caused by hypertension, as well as other comorbities that affect the geriatrics community. Using these metrics in a small population, we identify a statistical discrepancy between a control group and subjects with hypertension, neuropathy, diabetes, osteoporosis, arthritis, and several other common diseases which severely affect the geriatrics community.

Wavelet transform and real-time learning method for myoelectric signal in motion discrimination

International Nuclear Information System (INIS)

Liu Haihua; Chen Xinhao; Chen Yaguang

2005-01-01

This paper discusses the applicability of the Wavelet transform for analyzing an EMG signal and discriminating motion classes. In many previous works, researchers have dealt with steady EMG and have proposed suitable analyzing methods for the EMG, for example FFT and STFT. Therefore, it is difficult for the previous approaches to discriminate motions from the EMG in the different phases of muscle activity, i.e., pre-activity, in activity, postactivity phases, as well as the period of motion transition from one to another. In this paper, we introduce the Wavelet transform using the Coiflet mother wavelet into our real-time EMG prosthetic hand controller for discriminating motions from steady and unsteady EMG. A preliminary experiment to discriminate three hand motions from four channel EMG in the initial pre-activity and in activity phase is carried out to show the effectiveness of the approach. However, future research efforts are necessary to discriminate more motions much precisely

Research on feature extraction and classification of AE signals of fibers' tensile failure based on HHT and SVM

Directory of Open Access Journals (Sweden)

Yanding SHEN

2016-10-01

Full Text Available In order to study the feature extraction and recognition method of fibers' tensile failure, AE technology is used to collect AE signals of fiber bundle's tensile fracture of two kinds of fibers of Aramid 1313 and viscose. A transform called wavelet is used to deal with the signals to reduce noise. A method called Hilbert-Huang transform (HHT is used to extract characteristic frequencies of the signals after the noise is reduced. And a classification method called Least Squares support vector machines (LSSVM is used for the classification and recognition of characteristic frequencies of the two kinds of fibers. The results show that wavelet de-noise method can reduce some noise of the signals. Hilbert spectrum can reflect fracture circumstances of the two kinds of fibers in the time dimension to some extent. Characteristic frequencies' extraction can be done from marginal spectrum. The LSSVM can be used for the classification and recognition of characteristic frequencies. The recognition rates of Aramid 1313 and viscose reach 40%, 80% respectively, and the total recognition rate reaches 60%.

Simultaneous EEG and EMG biofeedback for peak performance in musicians.

Science.gov (United States)

Markovska-Simoska, Silvana; Pop-Jordanova, Nada; Georgiev, Dejan

2008-07-01

The aim of this study was to determine the effects of alpha neurofeedback and EMG biofeedback protocols for improvement of musical performance in violinists. The sample consisted of 12 music students (10 violinists and 2 viola players) from the Faculty of Music, Skopje (3 males, mean age of 20 +/- 0 and 9 females, mean age = 20.89 +/- 2.98). Six of them had a low alpha peak frequency (APF) ( 10 Hz). The sample was randomized in two groups. The students from the experimental group participated in 20 sessions of biofeedback (alpha/EMG), combined with music practice, while the students from the control group did only music practice. Average absolute power, interhemispheric coherence in the alpha band, alpha peak frequency (APF), individual alpha band width (IABW), amount of alpha suppression (AAS) and surface forehead integrated EMG power (IEMG), as well as a score on musical performance and inventories measuring anxiety, were assessed. Alpha-EEG/EMG-biofeedback was associated with a significant increase in average alpha power, APF and IABW in all the participants and with decreases in IEMG only in high-APF musicians. The biofeedback training success was positively correlated with the alpha power, IcoH, APF, IABW and baseline level of APF and IABW. Alpha-EEG/EMG biofeedback is capable of increasing voluntary self-regulation and the quality of musical performance. The efficiency of biofeedback training depends on the baseline EEG alpha activity status, in particular the APF.

Myopathic EMG findings and type II muscle fiber atrophy in patients with Lambert-Eaton myasthenic syndrome

DEFF Research Database (Denmark)

Crone, Clarissa; Christiansen, Ingelise; Vissing, John

2013-01-01

Lambert-Eaton myasthenic syndrome (LEMS) is a rare condition, which may mimic myopathy. A few reports have described that EMG in LEMS may show changes compatible with myopathy, and muscle biopsies have been described with type II as well as type I atrophy. The EMG results were, however, based on ...... on qualitative EMG examination and the histopathological methods were not always clear. The objective of this study was to investigate if the previous EMG findings could be confirmed with quantitative EMG (QEMG) and to describe muscle histology in LEMS.......Lambert-Eaton myasthenic syndrome (LEMS) is a rare condition, which may mimic myopathy. A few reports have described that EMG in LEMS may show changes compatible with myopathy, and muscle biopsies have been described with type II as well as type I atrophy. The EMG results were, however, based...

Muscle MRI STIR signal intensity and atrophy are correlated to focal lower limb neuropathy severity.

Science.gov (United States)

Deroide, N; Bousson, V; Mambre, L; Vicaut, E; Laredo, J D; Kubis, Nathalie

2015-03-01

The objective is to determine if muscle MRI is useful for assessing neuropathy severity. Clinical, MRI and electromyography (EMG) examinations were performed in 17 patients with focal lower limb neuropathies. MRI Short Tau Inversion Recovery (STIR) signal intensity, amyotrophy, and muscle fatty infiltration measured after T1-weighted image acquisition, EMG spontaneous activity (SA), and maximal voluntary contraction (MVC) were graded using semiquantitative scores and quantitative scores for STIR signal intensity and were correlated to the Medical Research Council (MRC) score for testing muscle strength. Within this population, subgroups were selected according to severity (mild versus severe), duration (subacute versus chronic), and topography (distal versus proximal) of the neuropathy. EMG SA and MVC MRI amyotrophy and quantitative scoring of muscle STIR intensity were correlated with the MRC score. Moreover, MRI amyotrophy was significantly increased in severe, chronic, and proximal neuropathies along with fatty infiltration in chronic lesions. Muscle MRI atrophy and quantitative evaluation of signal intensity were correlated to MRC score in our study. Semiquantitative evaluation of muscle STIR signal was sensitive enough for detection of topography of the nerve lesion but was not suitable to assess severity. Muscle MRI could support EMG in chronic and proximal neuropathy, which showed poor sensitivity in these patients.

Muscle MRI STIR signal intensity and atrophy are correlated to focal lower limb neuropathy severity

Energy Technology Data Exchange (ETDEWEB)

Deroide, N.; Mambre, L.; Kubis, Nathalie [Service de Physiologie Clinique-Explorations Fonctionnelles, AP-HP, Hopital Lariboisiere, Paris (France); Universite Paris Diderot, Sorbonne Paris Cite France, Paris (France); Bousson, V.; Laredo, J.D. [Universite Paris Diderot, Sorbonne Paris Cite France, Paris (France); Radiologie Osteo-articulaire, AP-HP, Hopital Lariboisiere, Paris (France); Vicaut, E. [Universite Paris Diderot, Sorbonne Paris Cite France, Paris (France); URC, AP-HP, Hopital Lariboisiere, Paris (France)

2014-09-26

The objective is to determine if muscle MRI is useful for assessing neuropathy severity. Clinical, MRI and electromyography (EMG) examinations were performed in 17 patients with focal lower limb neuropathies. MRI Short Tau Inversion Recovery (STIR) signal intensity, amyotrophy, and muscle fatty infiltration measured after T1-weighted image acquisition, EMG spontaneous activity (SA), and maximal voluntary contraction (MVC) were graded using semiquantitative scores and quantitative scores for STIR signal intensity and were correlated to the Medical Research Council (MRC) score for testing muscle strength. Within this population, subgroups were selected according to severity (mild versus severe), duration (subacute versus chronic), and topography (distal versus proximal) of the neuropathy. EMG SA and MVC MRI amyotrophy and quantitative scoring of muscle STIR intensity were correlated with the MRC score. Moreover, MRI amyotrophy was significantly increased in severe, chronic, and proximal neuropathies along with fatty infiltration in chronic lesions. Muscle MRI atrophy and quantitative evaluation of signal intensity were correlated to MRC score in our study. Semiquantitative evaluation of muscle STIR signal was sensitive enough for detection of topography of the nerve lesion but was not suitable to assess severity. Muscle MRI could support EMG in chronic and proximal neuropathy, which showed poor sensitivity in these patients. (orig.)

Speech Enhancement by Classification of Noisy Signals Decomposed Using NMF and Wiener Filtering

DEFF Research Database (Denmark)

Fakhry, Mahmoud; Poorjam, Amir Hossein; Christensen, Mads Græsbøll

2018-01-01

are identified in the cepstral domain using the trained classifier. We apply unsupervised NMF followed by Wiener filtering for the decomposition, and use a support vector machine trained on the mel-frequency cepstral coefficients of the parts of training speech and noise signals for the classification...

EMG biofeedback training in adult attention-deficit/hyperactivity disorder: An active (control) training?

Science.gov (United States)

Barth, Beatrix; Mayer, Kerstin; Strehl, Ute; Fallgatter, Andreas J; Ehlis, Ann-Christine

2017-06-30

The present study aimed at revealing neurophysiological effects induced by electromyography (EMG) based biofeedback, considered as a semi-active control condition in neurofeedback studies, in adult attention-deficit/hyperactivity disorder (ADHD) patients. 20 adult ADHD patients trained their muscle activity in the left and right supraspinatus muscle over the course of 30 EMG biofeedback sessions. Changes induced by the EMG feedback were evaluated at a clinical and neurophysiological level; additionally, the relation between changes in EEG activity recorded at the vertex over the training course and changes of symptom severity over the treatment course were assessed in order to investigate the mechanisms underlying clinical effects of EMG biofeedback. Participants showed significant behavioral improvements on a self-rating scale. There was a significant increase in alpha power, but no significant changes in the delta frequency range; changes in the theta and beta frequency range were not significant after adjustment for multiple comparisons. No statistically significant correlation was found between changes in EEG frequency bands and changes in ADHD symptoms. The current results assessed by means of a single-electrode EEG constitute a starting point regarding a clearer understanding of mechanisms underlying clinical effects of EMG biofeedback. Although we did not reveal systematic effects induced by EMG feedback on brain activity it remains an open question whether EMG biofeedback induces changes in brain regions or parameters we did not gather in the present study (e.g. motor cortex). Copyright © 2017 Elsevier B.V. All rights reserved.

Design of a portable, intrinsically safe multichannel acquisition system for high-resolution, real-time processing HD-sEMG.

Science.gov (United States)

Barone, Umberto; Merletti, Roberto

2013-08-01

A compact and portable system for real-time, multichannel, HD-sEMG acquisition is presented. The device is based on a modular, multiboard approach for scalability and to optimize power consumption for battery operating mode. The proposed modular approach allows us to configure the number of sEMG channels from 64 to 424. A plastic-optical-fiber-based 10/100 Ethernet link is implemented on a field-programmable gate array (FPGA)-based board for real-time, safety data transmission toward a personal computer or laptop for data storage and offline analysis. The high-performance A/D conversion stage, based on 24-bit ADC, allows us to automatically serialize the samples and transmits them on a single SPI bus connecting a sequence of up to 14 ADC chips in chain mode. The prototype is configured to work with 64 channels and a sample frequency of 2.441 ksps (derived from 25-MHz clock source), corresponding to a real data throughput of 3 Mbps. The prototype was assembled to demonstrate the available features (e.g., scalability) and evaluate the expected performances. The analog front end board could be dynamically configured to acquire sEMG signals in monopolar or single differential mode by means of FPGA I/O interface. The system can acquire continuously 64 channels for up to 5 h with a lightweight battery pack of 7.5 Vdc/2200 mAh. A PC-based application was also developed, by means of the open source Qt Development Kit from Nokia, for prototype characterization, sEMG measurements, and real-time visualization of 2-D maps.

Estimation of distal arm joint angles from EMG and shoulder orientation for transhumeral prostheses.

Science.gov (United States)

Akhtar, Aadeel; Aghasadeghi, Navid; Hargrove, Levi; Bretl, Timothy

2017-08-01

In this paper, we quantify the extent to which shoulder orientation, upper-arm electromyography (EMG), and forearm EMG are predictors of distal arm joint angles during reaching in eight subjects without disability as well as three subjects with a unilateral transhumeral amputation and targeted reinnervation. Prior studies have shown that shoulder orientation and upper-arm EMG, taken separately, are predictors of both elbow flexion/extension and forearm pronation/supination. We show that, for eight subjects without disability, shoulder orientation and upper-arm EMG together are a significantly better predictor of both elbow flexion/extension during unilateral (R 2 =0.72) and mirrored bilateral (R 2 =0.72) reaches and of forearm pronation/supination during unilateral (R 2 =0.77) and mirrored bilateral (R 2 =0.70) reaches. We also show that adding forearm EMG further improves the prediction of forearm pronation/supination during unilateral (R 2 =0.82) and mirrored bilateral (R 2 =0.75) reaches. In principle, these results provide the basis for choosing inputs for control of transhumeral prostheses, both by subjects with targeted motor reinnervation (when forearm EMG is available) and by subjects without target motor reinnervation (when forearm EMG is not available). In particular, we confirm that shoulder orientation and upper-arm EMG together best predict elbow flexion/extension (R 2 =0.72) for three subjects with unilateral transhumeral amputations and targeted motor reinnervation. However, shoulder orientation alone best predicts forearm pronation/supination (R 2 =0.88) for these subjects, a contradictory result that merits further study. Copyright © 2017 Elsevier Ltd. All rights reserved.

The recovery of repeated-sprint exercise is associated with PCr resynthesis, while muscle pH and EMG amplitude remain depressed.

Directory of Open Access Journals (Sweden)

Alberto Mendez-Villanueva

Full Text Available The physiological equivalents of power output maintenance and recovery during repeated-sprint exercise (RSE remain to be fully elucidated. In an attempt to improve our understanding of the determinants of RSE performance we therefore aimed to determine its recovery following exhaustive exercise (which affected intramuscular and neural factors concomitantly with those of intramuscular concentrations of adenosine triphosphate [ATP], phosphocreatine [PCr] and pH values and electromyography (EMG activity (a proxy for net motor unit activity changes. Eight young men performed 10, 6-s all-out sprints on a cycle ergometer, interspersed with 30 s of recovery, followed, after 6 min of passive recovery, by five 6-s sprints, again interspersed by 30 s of passive recovery. Biopsies of the vastus lateralis were obtained at rest, immediately after the first 10 sprints and after 6 min of recovery. EMG activity of the vastus lateralis was obtained from surface electrodes throughout exercise. Total work (TW, [ATP], [PCr], pH and EMG amplitude decreased significantly throughout the first ten sprints (P<0.05. After 6 min of recovery, TW during sprint 11 recovered to 86.3±7.7% of sprint 1. ATP and PCr were resynthesized to 92.6±6.0% and 85.3±10.3% of the resting value, respectively, but muscle pH and EMG amplitude remained depressed. PCr resynthesis was correlated with TW done in sprint 11 (r = 0.79, P<0.05 and TW done during sprints 11 to 15 (r = 0.67, P<0.05. There was a ∼2-fold greater decrease in the TW/EMG ratio in the last five sprints (sprint 11 to 15 than in the first five sprints (sprint 1 to 5 resulting in a disproportionate decrease in mechanical power (i.e., TW in relation to EMG. Thus, we conclude that the inability to produce power output during repeated sprints is mostly mediated by intramuscular fatigue signals probably related with the control of PCr metabolism.

Hand and finger dexterity as a function of skin temperature, EMG, and ambient condition.

Science.gov (United States)

Chen, Wen-Lin; Shih, Yuh-Chuan; Chi, Chia-Fen

2010-06-01

This article examines the changes in skin temperature (finger, hand, forearm), manual performance (hand dexterity and strength), and forearm surface electromyograph (EMG) through 40-min, 11 degrees C water cooling followed by 15-min, 34 degrees C water rewarming; additionally, it explores the relationship between dexterity and the factors of skin temperature, EMG, and ambient condition. Hand exposure in cold conditions is unavoidable and significantly affects manual performance. Two tasks requiring gross and fine dexterity were designed, namely, nut loosening and pin insertion, respectively. The nested-factorial design includes factors of gender, participant (nested within gender), immersion duration, muscle type (for EMG), and location (for skin temperature). The responses are changes in dexterity, skin temperature, normalized amplitude of EMG, and grip strength. Finally, factor analysis and stepwise regression are used to explore factors affecting hand and finger dexterity. Dexterity, EMG, and skin temperature fell with prolonged cooling, but the EMG of the flexor digitorum superficialis remained almost unchanged during the nut loosening task. All responses but the forearm skin temperature recovered to the baseline level at the end of rewarming. The three factors extracted by factor analysis are termed skin temperature, ambient condition, and EMG. They explain approximately two thirds of the variation of the linear models for both dexterities, and the factor of skin temperature is the most influential. Sustained cooling and warming significantly decreases and increases finger, hand, and forearm skin temperature. Dexterity, strength, and EMG are positively correlated to skin temperature. Therefore, keeping the finger, hand, and forearm warm is important to maintaining hand performance. The findings could be helpful to building safety guidelines for working in cold environments.

A Wireless sEMG Recording System and Its Application to Muscle Fatigue Detection

Science.gov (United States)

Chang, Kang-Ming; Liu, Shin-Hong; Wu, Xuan-Han

2012-01-01

Surface electromyography (sEMG) is an important measurement for monitoring exercise and fitness. Because if its high sampling frequency requirement, wireless transmission of sEMG data is a challenge. In this article a wireless sEMG measurement system with a sampling frequency of 2 KHz is developed based upon a MSP 430 microcontroller and Bluetooth transmission. Standard isotonic and isometric muscle contraction are clearly represented in the receiving user interface. Muscle fatigue detection is an important application of sEMG. Traditional muscle fatigue is detected from the median frequency of the sEMG power spectrum. The regression slope of the linear regression of median frequency is an important muscle fatigue index. A more negative slope value represents a higher muscle fatigue condition. To test the system performance, muscle fatigue detection was examined by having subjects run on a pedaled-multifunctional elliptical trainer for approximately 30 minutes at three loading levels. Ten subjects underwent a total of 60 exercise sessions to provide the experimental data. Results showed that the regression slope gradually decreases as expected, and there is a significant gender difference. PMID:22368481

A critical period of corticomuscular and EMG-EMG coherence detection in healthy infants aged 9-25weeks

DEFF Research Database (Denmark)

Ritterband-Rosenbaum, Anina; Herskind, Anna; Li, Xi

2017-01-01

The early postnatal development of functional corticospinal connections in human infants is not fully clarified. We used EEG and EMG to investigate the development of corticomuscular and intramuscular coherence as indicators of functional corticospinal connectivity in healthy infants aged 1-66 we...

Evoked EMG-based torque prediction under muscle fatigue in implanted neural stimulation

Science.gov (United States)

Hayashibe, Mitsuhiro; Zhang, Qin; Guiraud, David; Fattal, Charles

2011-10-01

In patients with complete spinal cord injury, fatigue occurs rapidly and there is no proprioceptive feedback regarding the current muscle condition. Therefore, it is essential to monitor the muscle state and assess the expected muscle response to improve the current FES system toward adaptive force/torque control in the presence of muscle fatigue. Our team implanted neural and epimysial electrodes in a complete paraplegic patient in 1999. We carried out a case study, in the specific case of implanted stimulation, in order to verify the corresponding torque prediction based on stimulus evoked EMG (eEMG) when muscle fatigue is occurring during electrical stimulation. Indeed, in implanted stimulation, the relationship between stimulation parameters and output torques is more stable than external stimulation in which the electrode location strongly affects the quality of the recruitment. Thus, the assumption that changes in the stimulation-torque relationship would be mainly due to muscle fatigue can be made reasonably. The eEMG was proved to be correlated to the generated torque during the continuous stimulation while the frequency of eEMG also decreased during fatigue. The median frequency showed a similar variation trend to the mean absolute value of eEMG. Torque prediction during fatigue-inducing tests was performed based on eEMG in model cross-validation where the model was identified using recruitment test data. The torque prediction, apart from the potentiation period, showed acceptable tracking performances that would enable us to perform adaptive closed-loop control through implanted neural stimulation in the future.

Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals.

Science.gov (United States)

Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Mubin, Marizan; Saad, Ismail

2016-01-01

In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification.

Movement Performance of Human-Robot Cooperation Control Based on EMG-Driven Hill-Type and Proportional Models for an Ankle Power-Assist Exoskeleton Robot.

Science.gov (United States)

Ao, Di; Song, Rong; Gao, JinWu

2017-08-01

Although the merits of electromyography (EMG)-based control of powered assistive systems have been certified, the factors that affect the performance of EMG-based human-robot cooperation, which are very important, have received little attention. This study investigates whether a more physiologically appropriate model could improve the performance of human-robot cooperation control for an ankle power-assist exoskeleton robot. To achieve the goal, an EMG-driven Hill-type neuromusculoskeletal model (HNM) and a linear proportional model (LPM) were developed and calibrated through maximum isometric voluntary dorsiflexion (MIVD). The two control models could estimate the real-time ankle joint torque, and HNM is more accurate and can account for the change of the joint angle and muscle dynamics. Then, eight healthy volunteers were recruited to wear the ankle exoskeleton robot and complete a series of sinusoidal tracking tasks in the vertical plane. With the various levels of assist based on the two calibrated models, the subjects were instructed to track the target displayed on the screen as accurately as possible by performing ankle dorsiflexion and plantarflexion. Two measurements, the root mean square error (RMSE) and root mean square jerk (RMSJ), were derived from the assistant torque and kinematic signals to characterize the movement performances, whereas the amplitudes of the recorded EMG signals from the tibialis anterior (TA) and the gastrocnemius (GAS) were obtained to reflect the muscular efforts. The results demonstrated that the muscular effort and smoothness of tracking movements decreased with an increase in the assistant ratio. Compared with LPM, subjects made lower physical efforts and generated smoother movements when using HNM, which implied that a more physiologically appropriate model could enable more natural and human-like human-robot cooperation and has potential value for improvement of human-exoskeleton interaction in future applications.

EEG signal classification based on artificial neural networks and amplitude spectra features

Science.gov (United States)

Chojnowski, K.; FrÄ czek, J.

BCI (called Brain-Computer Interface) is an interface that allows direct communication between human brain and an external device. It bases on EEG signal collection, processing and classification. In this paper a complete BCI system is presented which classifies EEG signal using artificial neural networks. For this purpose we used a multi-layered perceptron architecture trained with the RProp algorithm. Furthermore a simple multi-threaded method for automatic network structure optimizing was shown. We presented the results of our system in the opening and closing eyes recognition task. We also showed how our system could be used for controlling devices basing on imaginary hand movements.

Object Classification Based on Analysis of Spectral Characteristics of Seismic Signal Envelopes

Science.gov (United States)

Morozov, Yu. V.; Spektor, A. A.

2017-11-01

A method for classifying moving objects having a seismic effect on the ground surface is proposed which is based on statistical analysis of the envelopes of received signals. The values of the components of the amplitude spectrum of the envelopes obtained applying Hilbert and Fourier transforms are used as classification criteria. Examples illustrating the statistical properties of spectra and the operation of the seismic classifier are given for an ensemble of objects of four classes (person, group of people, large animal, vehicle). It is shown that the computational procedures for processing seismic signals are quite simple and can therefore be used in real-time systems with modest requirements for computational resources.

Specialized Nerve Tests: EMG, NCV and SSEP

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... Treatment Spondylolisthesis BLOG FIND A SPECIALIST Treatments Specialized Nerve Tests: EMG, NCV and SSEP Ajay Jawahar MD ... spinal cord is the thick, whitish bundle of nerve tissue that extends from the lowest part of ...

ABC optimized RBF network for classification of EEG signal for epileptic seizure identification

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Sandeep Kumar Satapathy

2017-03-01

Full Text Available The brain signals usually generate certain electrical signals that can be recorded and analyzed for detection in several brain disorder diseases. These small signals are expressly called as Electroencephalogram (EEG signals. This research work analyzes the epileptic disorder in human brain through EEG signal analysis by integrating the best attributes of Artificial Bee Colony (ABC and radial basis function networks (RBFNNs. We have used Discrete Wavelet Transform (DWT technique for extraction of potential features from the signal. In our study, for classification of these signals, in this paper, the RBFNNs have been trained by a modified version of ABC algorithm. In the modified ABC, the onlooker bees are selected based on binary tournament unlike roulette wheel selection of ABC. Additionally, kernels such as Gaussian, Multi-quadric, and Inverse-multi-quadric are used for measuring the effectiveness of the method in numerous mixtures of healthy segments, seizure-free segments, and seizure segments. Our experimental outcomes confirm that RBFNN with inverse-multi-quadric kernel trained with modified ABC is significantly better than RBFNNs with other kernels trained by ABC and modified ABC.

Knee joint angle affects EMG-force relationship in the vastus intermedius muscle.

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Saito, Akira; Akima, Hiroshi

2013-12-01

It is not understood how the knee joint angle affects the relationship between electromyography (EMG) and force of four individual quadriceps femoris (QF) muscles. The purpose of this study was to examine the effect of the knee joint angle on the EMG-force relationship of the four individual QF muscles, particularly the vastus intermedius (VI), during isometric knee extensions. Eleven healthy men performed 20-100% of maximal voluntary contraction (MVC) at knee joint angles of 90°, 120° and 150°. Surface EMG of the four QF synergists was recorded and normalized by the root mean square during MVC. The normalized EMG of the four QF synergists at a knee joint angle of 150° was significantly lower than that at 90° and 120° (P knee joint angle of 150°. Furthermore, the neuromuscular activation of the VI was the most sensitive to change in muscle length among the four QF synergistic muscles. Copyright © 2013 Elsevier Ltd. All rights reserved.

EMG monitoring during functional non-surgical therapy of Achilles tendon rupture.

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Hüfner, Tobias; Wohifarth, Kai; Fink, Matthias; Thermann, H; Rollnik, Jens D

2002-07-01

After surgical therapy of Achilles tendon rupture, neuromuscular changes may persist, even one year after surgery. We were interested whether these changes are also evident following a non-surgical functional therapy (Variostabil therapy boot/Adidas). Twenty-one patients with complete Achilles tendon rupture were enrolled in the study (mean age 38.5 years, range 24 to 60; 18 men, three women) and followed-up clinically and with surface EMG of the gastrocnemius muscles after four, eight, 12 weeks, and one year after rupture. EMG differences between the affected and non-affected side could only be observed at baseline and after four weeks following Achilles tendon rupture. The results from our study show that EMG changes are not found following non-surgical functional therapy.

Processing Electromyographic Signals to Recognize Words

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Jorgensen, C. C.; Lee, D. D.

2009-01-01

A recently invented speech-recognition method applies to words that are articulated by means of the tongue and throat muscles but are otherwise not voiced or, at most, are spoken sotto voce. This method could satisfy a need for speech recognition under circumstances in which normal audible speech is difficult, poses a hazard, is disturbing to listeners, or compromises privacy. The method could also be used to augment traditional speech recognition by providing an additional source of information about articulator activity. The method can be characterized as intermediate between (1) conventional speech recognition through processing of voice sounds and (2) a method, not yet developed, of processing electroencephalographic signals to extract unspoken words directly from thoughts. This method involves computational processing of digitized electromyographic (EMG) signals from muscle innervation acquired by surface electrodes under a subject's chin near the tongue and on the side of the subject s throat near the larynx. After preprocessing, digitization, and feature extraction, EMG signals are processed by a neural-network pattern classifier, implemented in software, that performs the bulk of the recognition task as described.

A Wireless sEMG Recording System and Its Application to Muscle Fatigue Detection

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Xuan-Han Wu

2012-01-01

Full Text Available Surface electromyography (sEMG is an important measurement for monitoring exercise and fitness. Because if its high sampling frequency requirement, wireless transmission of sEMG data is a challenge. In this article a wireless sEMG measurement system with a sampling frequency of 2 KHz is developed based upon a MSP 430 microcontroller and Bluetooth transmission. Standard isotonic and isometric muscle contraction are clearly represented in the receiving user interface. Muscle fatigue detection is an important application of sEMG. Traditional muscle fatigue is detected from the median frequency of the sEMG power spectrum. The regression slope of the linear regression of median frequency is an important muscle fatigue index. A more negative slope value represents a higher muscle fatigue condition. To test the system performance, muscle fatigue detection was examined by having subjects run on a pedaled-multifunctional elliptical trainer for approximately 30 minutes at three loading levels. Ten subjects underwent a total of 60 exercise sessions to provide the experimental data. Results showed that the regression slope gradually decreases as expected, and there is a significant gender difference.

Classification of Knee Joint Vibration Signals Using Bivariate Feature Distribution Estimation and Maximal Posterior Probability Decision Criterion

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Fang Zheng

2013-04-01

Full Text Available Analysis of knee joint vibration or vibroarthrographic (VAG signals using signal processing and machine learning algorithms possesses high potential for the noninvasive detection of articular cartilage degeneration, which may reduce unnecessary exploratory surgery. Feature representation of knee joint VAG signals helps characterize the pathological condition of degenerative articular cartilages in the knee. This paper used the kernel-based probability density estimation method to model the distributions of the VAG signals recorded from healthy subjects and patients with knee joint disorders. The estimated densities of the VAG signals showed explicit distributions of the normal and abnormal signal groups, along with the corresponding contours in the bivariate feature space. The signal classifications were performed by using the Fisher’s linear discriminant analysis, support vector machine with polynomial kernels, and the maximal posterior probability decision criterion. The maximal posterior probability decision criterion was able to provide the total classification accuracy of 86.67% and the area (Az of 0.9096 under the receiver operating characteristics curve, which were superior to the results obtained by either the Fisher’s linear discriminant analysis (accuracy: 81.33%, Az: 0.8564 or the support vector machine with polynomial kernels (accuracy: 81.33%, Az: 0.8533. Such results demonstrated the merits of the bivariate feature distribution estimation and the superiority of the maximal posterior probability decision criterion for analysis of knee joint VAG signals.

Application of multiple signal classification algorithm to frequency estimation in coherent dual-frequency lidar

Science.gov (United States)

Li, Ruixiao; Li, Kun; Zhao, Changming

2018-01-01

Coherent dual-frequency Lidar (CDFL) is a new development of Lidar which dramatically enhances the ability to decrease the influence of atmospheric interference by using dual-frequency laser to measure the range and velocity with high precision. Based on the nature of CDFL signals, we propose to apply the multiple signal classification (MUSIC) algorithm in place of the fast Fourier transform (FFT) to estimate the phase differences in dual-frequency Lidar. In the presence of Gaussian white noise, the simulation results show that the signal peaks are more evident when using MUSIC algorithm instead of FFT in condition of low signal-noise-ratio (SNR), which helps to improve the precision of detection on range and velocity, especially for the long distance measurement systems.

Extruded Bread Classification on the Basis of Acoustic Emission Signal With Application of Artificial Neural Networks

Science.gov (United States)

Świetlicka, Izabela; Muszyński, Siemowit; Marzec, Agata

2015-04-01

The presented work covers the problem of developing a method of extruded bread classification with the application of artificial neural networks. Extruded flat graham, corn, and rye breads differening in water activity were used. The breads were subjected to the compression test with simultaneous registration of acoustic signal. The amplitude-time records were analyzed both in time and frequency domains. Acoustic emission signal parameters: single energy, counts, amplitude, and duration acoustic emission were determined for the breads in four water activities: initial (0.362 for rye, 0.377 for corn, and 0.371 for graham bread), 0.432, 0.529, and 0.648. For classification and the clustering process, radial basis function, and self-organizing maps (Kohonen network) were used. Artificial neural networks were examined with respect to their ability to classify or to cluster samples according to the bread type, water activity value, and both of them. The best examination results were achieved by the radial basis function network in classification according to water activity (88%), while the self-organizing maps network yielded 81% during bread type clustering.

Effects of load on good morning kinematics and EMG activity

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Andrew David Vigotsky

2015-01-01

Full Text Available Many strength and conditioning coaches utilize the good morning (GM to strengthen the hamstrings and spinal erectors. However, little research exists on its electromyography (EMG activity and kinematics, and how these variables change as a function of load. The purpose of this investigation was to examine how estimated hamstring length, integrated EMG (IEMG activity of the hamstrings and spinal erectors, and kinematics of the lumbar spine, hip, knee, and ankle are affected by changes in load. Fifteen trained male participants (age = 24.6 ± 5.3 years; body mass = 84.7 ± 11.3 kg; height = 180.9 ± 6.8 cm were recruited for this study. Participants performed five sets of the GM, utilizing 50, 60, 70, 80, and 90% of one-repetition maximum (1RM in a randomized fashion. IEMG activity of hamstrings and spinal erectors tended to increase with load. Knee flexion increased with load on all trials. Estimated hamstring length decreased with load. However, lumbar flexion, hip flexion, and plantar flexion experienced no remarkable changes between trials. These data provide insight as to how changing the load of the GM affects EMG activity, kinematic variables, and estimated hamstring length. Implications for hamstring injury prevention are discussed. More research is needed for further insight as to how load affects EMG activity and kinematics of other exercises.

Electromyographic Grasp Recognition for a Five Fingered Robotic Hand

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Nayan M. Kakoty

2012-09-01

Full Text Available This paper presents classification of grasp types based on surface electromyographic signals. Classification is through radial basis function kernel support vector machine using sum of wavelet decomposition coefficients of the EMG signals. In a study involving six subjects, we achieved an average recognition rate of 86%. The electromyographic grasp recognition together with a 8-bit microcontroller has been employed to control a fivefingered robotic hand to emulate six grasp types used during 70% daily living activities.

Embodied simulation as part of affective evaluation processes: task dependence of valence concordant EMG activity.

Science.gov (United States)

Weinreich, André; Funcke, Jakob Maria

2014-01-01

Drawing on recent findings, this study examines whether valence concordant electromyography (EMG) responses can be explained as an unconditional effect of mere stimulus processing or as somatosensory simulation driven by task-dependent processing strategies. While facial EMG over the Corrugator supercilii and the Zygomaticus major was measured, each participant performed two tasks with pictures of album covers. One task was an affective evaluation task and the other was to attribute the album covers to one of five decades. The Embodied Emotion Account predicts that valence concordant EMG is more likely to occur if the task necessitates a somatosensory simulation of the evaluative meaning of stimuli. Results support this prediction with regard to Corrugator supercilii in that valence concordant EMG activity was only present in the affective evaluation task but not in the non-evaluative task. Results for the Zygomaticus major were ambiguous. Our findings are in line with the view that EMG activity is an embodied part of the evaluation process and not a mere physical outcome.

Control of leg movements driven by EMG activity of shoulder muscles

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Valentina eLa Scaleia

2014-10-01

Full Text Available During human walking there exists a functional neural coupling between arms and legs, and between cervical and lumbosacral pattern generators. Here we present a novel approach for associating the electromyographic (EMG activity from upper limb muscles with leg kinematics. Our methodology takes advantage of the high involvement of shoulder muscles in most locomotor-related movements and of the natural coordination between arms and legs. Nine healthy subjects were asked to walk at different constant and variable speeds (3-5 km/h, while EMG activity of shoulder (deltoid muscles and the kinematics of walking were recorded. To ensure a high level of EMG activity in deltoid, the subjects performed slightly larger arm swinging than they usually do. The temporal structure of the burst-like EMG activity was used to predict the spatiotemporal kinematic pattern of the forthcoming step. A comparison of actual and predicted stride leg kinematics showed a high degree of correspondence (r>0.9. This algorithm has been also implemented in pilot experiments for controlling avatar walking in a virtual reality setup and an exoskeleton during overground stepping. The proposed approach may have important implications for the design of human-machine interfaces and neuroprosthetic technologies such as those of assistive lower limb exoskeletons.

Effect of clinical parameters on the control of myoelectric robotic prosthetic hands.

Science.gov (United States)

Atzori, Manfredo; Gijsberts, Arjan; Castellini, Claudio; Caputo, Barbara; Hager, Anne-Gabrielle Mittaz; Elsig, Simone; Giatsidis, Giorgio; Bassetto, Franco; Müller, Henning

2016-01-01

Improving the functionality of prosthetic hands with noninvasive techniques is still a challenge. Surface electromyography (sEMG) currently gives limited control capabilities; however, the application of machine learning to the analysis of sEMG signals is promising and has recently been applied in practice, but many questions still remain. In this study, we recorded the sEMG activity of the forearm of 11 male subjects with transradial amputation who were mentally performing 40 hand and wrist movements. The classification performance and the number of independent movements (defined as the subset of movements that could be distinguished with >90% accuracy) were studied in relationship to clinical parameters related to the amputation. The analysis showed that classification accuracy and the number of independent movements increased significantly with phantom limb sensation intensity, remaining forearm percentage, and temporal distance to the amputation. The classification results suggest the possibility of naturally controlling up to 11 movements of a robotic prosthetic hand with almost no training. Knowledge of the relationship between classification accuracy and clinical parameters adds new information regarding the nature of phantom limb pain as well as other clinical parameters, and it can lay the foundations for future "functional amputation" procedures in surgery.

Ultra low-power biomedical signal processing : An analog wavelet filter approach for pacemakers

NARCIS (Netherlands)

Pavlík Haddad, S.A.

2006-01-01

The purpose of this thesis is to describe novel signal processing methodologies and analog integrated circuit techniques for low-power biomedical systems. Physiological signals, such as the electrocardiogram (ECG), the electroencephalogram (EEG) and the electromyogram (EMG) are mostly

EEG?EMG polygraphic study of dystonia and myoclonus in a case of Creutzfeldt?Jakob disease ?

OpenAIRE

Hashimoto, Takao; Iwahashi, Teruaki; Ishii, Wataru; Yamamoto, Kanji; Ikeda, Shu-ichi

2015-01-01

We report on a patient with sporadic Creutzfeldt–Jakob disease (CJD) who showed dystonia, periodic myoclonus, and periodic sharp wave complexes (PSWCs) on EEG. The EEG–EMG polygraphic study revealed that dystonia appeared without relation to periodic myoclonus and PSWCs and that dystonia EMGs were strongly suppressed after periodic myoclonus EMGs. These findings suggest that dystonia has a pathogenesis different from that of periodic myoclonus and PSWCs, but dystonia and periodic myoclonus ma...

The effect of hip abduction on the EMG activity of vastus medialis obliquus, vastus lateralis longus and vastus lateralis obliquus in healthy subjects

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Arakaki Juliano

2006-07-01

Full Text Available Abstract Study design Controlled laboratory study. Objectives The purposes of this paper were to investigate (d whether vastus medialis obliquus (VMO, vastus lateralis longus (VLL and vastus lateralis obliquus (VLO EMG activity can be influenced by hip abduction performed by healthy subjects. Background Some clinicians contraindicate hip abduction for patellofemoral patients (with based on the premise that hip abduction could facilitate the VLL muscle activation leading to a VLL and VMO imbalance Methods and measures Twenty-one clinically healthy subjects were involved in the study, 10 women and 11 men (aged X = 23.3 ± 2.9. The EMG signals were collected using a computerized EMG VIKING II, with 8 channels and three pairs of surface electrodes. EMG activity was obtained from MVIC knee extension at 90° of flexion in a seated position and MVIC hip abduction at 0° and 30° with patients in side-lying position with the knee in full extension. The data were normalized in the MVIC knee extension at 50° of flexion in a seated position, and were submitted to ANOVA test with subsequent application of the Bonferroni multiple comparisons analysis test. The level of significance was defined as p ≤ 0.05. Results The VLO muscle demonstrated a similar pattern to the VMO muscle showing higher EMG activity in MVIC knee extension at 90° of flexion compared with MVIC hip abduction at 0° and 30° of abduction for male (p Conclusion The results showed that no selective EMG activation was observed when comparison was made between the VMO, VLL and VLO muscles while performing MVIC hip abduction at 0° and 30° of abduction and MVIC knee extension at 90° of flexion in both male and female subjects. Our findings demonstrate that hip abduction do not facilitated VLL and VLO activity in relation to the VMO, however, this study included only healthy subjects performing maximum voluntary isometric contraction contractions, therefore much remains to be discovered by

The effect of experimental stress and experimental occlusal interference on masseteric EMG activity.

Science.gov (United States)

McGlynn, F D; Bichajian, C; Tira, D E; Lundeen, H C; Mahan, P E; Nicholas, B V

1989-01-01

This experiment attempted to study the separate and combined effects of occlusal interference and transient stress on masseteric activity among eight nonclinical human subjects. Before each of two sessions, subjects were fitted with an occlusal interference or an occlusally inert (control) molar clasp. During each session they viewed horrific and idyllic videotapes while masseter EMG was recorded bilaterally. Electrodermal measures validated that the horrific videotapes were stressful. Studies showed that the occlusal variable worked less well. The EMG was elevated contralateral to both clasps and during videotape viewing. The EMG effects from videotape viewing were relatively pronounced without the occlusal interference. Research implications are discussed.

Corticomuscular transmission of tremor signals by propriospinal neurons in Parkinson's disease.

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Manzhao Hao

Full Text Available Cortical oscillatory signals of single and double tremor frequencies act together to cause tremor in the peripheral limbs of patients with Parkinson's disease (PD. But the corticospinal pathway that transmits the tremor signals has not been clarified, and how alternating bursts of antagonistic muscle activations are generated from the cortical oscillatory signals is not well understood. This paper investigates the plausible role of propriospinal neurons (PN in C3-C4 in transmitting the cortical oscillatory signals to peripheral muscles. Kinematics data and surface electromyogram (EMG of tremor in forearm were collected from PD patients. A PN network model was constructed based on known neurophysiological connections of PN. The cortical efferent signal of double tremor frequencies were integrated at the PN network, whose outputs drove the muscles of a virtual arm (VA model to simulate tremor behaviors. The cortical efferent signal of single tremor frequency actuated muscle spindles. By comparing tremor data of PD patients and the results of model simulation, we examined two hypotheses regarding the corticospinal transmission of oscillatory signals in Parkinsonian tremor. Hypothesis I stated that the oscillatory cortical signals were transmitted via the mono-synaptic corticospinal pathways bypassing the PN network. The alternative hypothesis II stated that they were transmitted by way of PN multi-synaptic corticospinal pathway. Simulations indicated that without the PN network, the alternating burst patterns of antagonistic muscle EMGs could not be reliably generated, rejecting the first hypothesis. However, with the PN network, the alternating burst patterns of antagonist EMGs were naturally reproduced under all conditions of cortical oscillations. The results suggest that cortical commands of single and double tremor frequencies are further processed at PN to compute the alternating burst patterns in flexor and extensor muscles, and the

Corticomuscular transmission of tremor signals by propriospinal neurons in Parkinson's disease.

Science.gov (United States)

Hao, Manzhao; He, Xin; Xiao, Qin; Alstermark, Bror; Lan, Ning

2013-01-01

Cortical oscillatory signals of single and double tremor frequencies act together to cause tremor in the peripheral limbs of patients with Parkinson's disease (PD). But the corticospinal pathway that transmits the tremor signals has not been clarified, and how alternating bursts of antagonistic muscle activations are generated from the cortical oscillatory signals is not well understood. This paper investigates the plausible role of propriospinal neurons (PN) in C3-C4 in transmitting the cortical oscillatory signals to peripheral muscles. Kinematics data and surface electromyogram (EMG) of tremor in forearm were collected from PD patients. A PN network model was constructed based on known neurophysiological connections of PN. The cortical efferent signal of double tremor frequencies were integrated at the PN network, whose outputs drove the muscles of a virtual arm (VA) model to simulate tremor behaviors. The cortical efferent signal of single tremor frequency actuated muscle spindles. By comparing tremor data of PD patients and the results of model simulation, we examined two hypotheses regarding the corticospinal transmission of oscillatory signals in Parkinsonian tremor. Hypothesis I stated that the oscillatory cortical signals were transmitted via the mono-synaptic corticospinal pathways bypassing the PN network. The alternative hypothesis II stated that they were transmitted by way of PN multi-synaptic corticospinal pathway. Simulations indicated that without the PN network, the alternating burst patterns of antagonistic muscle EMGs could not be reliably generated, rejecting the first hypothesis. However, with the PN network, the alternating burst patterns of antagonist EMGs were naturally reproduced under all conditions of cortical oscillations. The results suggest that cortical commands of single and double tremor frequencies are further processed at PN to compute the alternating burst patterns in flexor and extensor muscles, and the neuromuscular dynamics

An isometric muscle force estimation framework based on a high-density surface EMG array and an NMF algorithm

Science.gov (United States)

Huang, Chengjun; Chen, Xiang; Cao, Shuai; Qiu, Bensheng; Zhang, Xu

2017-08-01

Objective. To realize accurate muscle force estimation, a novel framework is proposed in this paper which can extract the input of the prediction model from the appropriate activation area of the skeletal muscle. Approach. Surface electromyographic (sEMG) signals from the biceps brachii muscle during isometric elbow flexion were collected with a high-density (HD) electrode grid (128 channels) and the external force at three contraction levels was measured at the wrist synchronously. The sEMG envelope matrix was factorized into a matrix of basis vectors with each column representing an activation pattern and a matrix of time-varying coefficients by a nonnegative matrix factorization (NMF) algorithm. The activation pattern with the highest activation intensity, which was defined as the sum of the absolute values of the time-varying coefficient curve, was considered as the major activation pattern, and its channels with high weighting factors were selected to extract the input activation signal of a force estimation model based on the polynomial fitting technique. Main results. Compared with conventional methods using the whole channels of the grid, the proposed method could significantly improve the quality of force estimation and reduce the electrode number. Significance. The proposed method provides a way to find proper electrode placement for force estimation, which can be further employed in muscle heterogeneity analysis, myoelectric prostheses and the control of exoskeleton devices.

Advanced Physiological Estimation of Cognitive Status. Part 2

Science.gov (United States)

2011-05-24

fatigue, overload) Technology Transfer Opportunity Technology from PDT – Methods to acquire various physiological signals (EEG, EOG , EMG, ECG, etc...Integrated Hardware for Experiments EEG Sensor Array EOG Sensor Array Eye Tracker Amplifiers and Signal Conditioners Laptop Computer...REPORT Advanced Physiological Estimation of Cognitive Status - Part II 14. ABSTRACT 16. SECURITY CLASSIFICATION OF: This report describes ongoing work

A stretchable electrode array for non-invasive, skin-mounted measurement of electrocardiography (ECG), electromyography (EMG) and electroencephalography (EEG).

Science.gov (United States)

Ma, Rui; Kim, Dae-Hyeong; McCormick, Martin; Coleman, Todd; Rogers, John

2010-01-01

This paper reports a class of stretchable electrode array capable of intimate, conformal integration onto the curvilinear surfaces of skin on the human body. The designs employ conventional metallic conductors but in optimized mechanical layouts, on soft, thin elastomeric substrates. These devices exhibit an ability to record spontaneous EEG activity even without conductive electrolyte gels, with recorded alpha rhythm responses that are 40% stronger than those collected using conventional tin electrodes and gels under otherwise similar conditions. The same type of device can also measure high quality ECG and EMG signals. The results suggest broad utility for skin-mounted measurements of electrical activity in the body, with advantages in signal levels, wearability and modes of integration compared to alternatives.

EMG analysis in 78 cases with motor neuron disease

Institute of Scientific and Technical Information of China (English)

Zhang Qiubin

2000-01-01

This paper analysed the FMGs of 78 cases with the motor neuron disease(MND). The EMG of all patients showed following characteristics that the average duration of wave prolonged, the average voltage increased and it was found that fibrillation and fasciculatton potentials appeared spontaneously. The fibrillation potential of ENG waa related to course of disease. In the patients whose course of disease was short, the fibri llation potential increased obviously, while in the cases of chronic MND, It usually decreased. The motor nerve conduction velocity of most pa tients (41%) reduced, however, the sensory nerve conduction velocity was normal but two. We reviewed some references about EMG of the motor neuron disease and discussed their characteristics and mechanism

Changes in force, surface and motor unit EMG during post-exercise development of low frequency fatigue in vastus lateralis muscle

NARCIS (Netherlands)

de Ruiter, C.J.; Elzinga, M.J.; Verdijk, P.W.L.; van Mechelen, W.; de Haan, A.

2005-01-01

We investigated the effects of low frequency fatigue (LFF) on post-exercise changes in rectified surface EMG (rsEMG) and single motor unit EMG (smuEMG) in vastus lateralis muscle (n=9). On two experimental days the knee extensors were fatigued with a 60-s-isometric contraction (exercise) at 50%

Effect of toe extension on EMG of triceps surae muscles during isometric dorsiflexion.

Science.gov (United States)

Siddiqi, Ariba; Arjunan, Sridhar P; Kumar, Dinesh

2016-12-01

The protocol for estimating force of contraction by triceps surae (TS) muscles requires the immobilization of the ankle during dorsiflexion and plantar flexion. However, large variability in the results has been observed. To identify the cause of this variability, experiments were conducted where ankle dorsiflexion force and electromyogram (EMG) of the TS were recorded under two conditions: (i) toes were strapped and (ii) toes were unstrapped, with all other conditions such as immobilization of the ankle remaining unchanged. The root mean square (RMS) of the EMG and the force were analyzed and one-tail Student's t-test was performed for significance between the two conditions. The RMS of the EMG from TS muscles was found to be significantly higher (~55%) during dorsiflexion with toes unstrapped compared with when the toes were strapped. The torque corresponding to dorsiflexion was also higher with toes unstrapped. Our study has shown that it is important to strap the toes when measuring the torque at the ankle and EMG of the TS muscles.

Origin of the low-level EMG during the silent period following transcranial magnetic stimulation

DEFF Research Database (Denmark)

Butler, Jane E; Petersen, Nicolas C; Herbert, Robert D

2012-01-01

OBJECTIVE: The cortical silent period refers to a period of near silence in the electromyogram (EMG) after transcranial magnetic stimulation (TMS) of the motor cortex during contraction. However, low-level EMG of unknown origin is often present. We hypothesised that it arises through spinal...

On using the Multiple Signal Classification algorithm to study microbaroms

Science.gov (United States)

Marcillo, O. E.; Blom, P. S.; Euler, G. G.

2016-12-01

Multiple Signal Classification (MUSIC) (Schmidt, 1986) is a well-known high-resolution algorithm used in array processing for parameter estimation. We report on the application of MUSIC to infrasonic array data in a study of the structure of microbaroms. Microbaroms can be globally observed and display energy centered around 0.2 Hz. Microbaroms are an infrasonic signal generated by the non-linear interaction of ocean surface waves that radiate into the ocean and atmosphere as well as the solid earth in the form of microseisms. Microbaroms sources are dynamic and, in many cases, distributed in space and moving in time. We assume that the microbarom energy detected by an infrasonic array is the result of multiple sources (with different back-azimuths) in the same bandwidth and apply the MUSIC algorithm accordingly to recover the back-azimuth and trace velocity of the individual components. Preliminary results show that the multiple component assumption in MUSIC allows one to resolve the fine structure in the microbarom band that can be related to multiple ocean surface phenomena.

An Analysis of Intrinsic and Extrinsic Hand Muscle EMG for Improved Pattern Recognition Control.

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Adewuyi, Adenike A; Hargrove, Levi J; Kuiken, Todd A

2016-04-01

Pattern recognition control combined with surface electromyography (EMG) from the extrinsic hand muscles has shown great promise for control of multiple prosthetic functions for transradial amputees. There is, however, a need to adapt this control method when implemented for partial-hand amputees, who possess both a functional wrist and information-rich residual intrinsic hand muscles. We demonstrate that combining EMG data from both intrinsic and extrinsic hand muscles to classify hand grasps and finger motions allows up to 19 classes of hand grasps and individual finger motions to be decoded, with an accuracy of 96% for non-amputees and 85% for partial-hand amputees. We evaluated real-time pattern recognition control of three hand motions in seven different wrist positions. We found that a system trained with both intrinsic and extrinsic muscle EMG data, collected while statically and dynamically varying wrist position increased completion rates from 73% to 96% for partial-hand amputees and from 88% to 100% for non-amputees when compared to a system trained with only extrinsic muscle EMG data collected in a neutral wrist position. Our study shows that incorporating intrinsic muscle EMG data and wrist motion can significantly improve the robustness of pattern recognition control for application to partial-hand prosthetic control.

Differences in the EMG pattern of lea muscle activation during locomotion in Parkinson's disease

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Albani, G; Sandrini, G; Kunig, G; Martin-Soelch, C; Mauro, A; Pignatti, R; Pacchetti, C; Dietz, [No Value; Leenders, KL

2003-01-01

In this pilot study, EMG patterns of leg muscle activation were studied in five parkinsonian patients with (B1) and five without (B2) freezing. Gastrocnemius medialis (GM) and tibialis anterior (TA) activity was analysed, by means of surface electromyography (EMG), during treadmill walking at two

EEG–EMG polygraphic study of dystonia and myoclonus in a case of Creutzfeldt–Jakob disease

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Takao Hashimoto

2015-01-01

Full Text Available We report on a patient with sporadic Creutzfeldt–Jakob disease (CJD who showed dystonia, periodic myoclonus, and periodic sharp wave complexes (PSWCs on EEG. The EEG–EMG polygraphic study revealed that dystonia appeared without relation to periodic myoclonus and PSWCs and that dystonia EMGs were strongly suppressed after periodic myoclonus EMGs. These findings suggest that dystonia has a pathogenesis different from that of periodic myoclonus and PSWCs, but dystonia and periodic myoclonus may be generated through the sensorimotor cortex in CJD.

The Response of Hyperkinesis to EMG Biofeedback.

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Haight, Maryellen J.; And Others

A study was conducted involving eight hyperkinetic males (11-15 years old) to determine if Ss receiving electromyography (EMG) biofeedback training would show a reduction in frontalis muscle tension, hyperactivity, and lability, and increases in self-esteem and visual and auditory attention span. Individual 45- and 30-minute relaxation exercises…

Multimodal fusion framework: a multiresolution approach for emotion classification and recognition from physiological signals.

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Verma, Gyanendra K; Tiwary, Uma Shanker

2014-11-15

The purpose of this paper is twofold: (i) to investigate the emotion representation models and find out the possibility of a model with minimum number of continuous dimensions and (ii) to recognize and predict emotion from the measured physiological signals using multiresolution approach. The multimodal physiological signals are: Electroencephalogram (EEG) (32 channels) and peripheral (8 channels: Galvanic skin response (GSR), blood volume pressure, respiration pattern, skin temperature, electromyogram (EMG) and electrooculogram (EOG)) as given in the DEAP database. We have discussed the theories of emotion modeling based on i) basic emotions, ii) cognitive appraisal and physiological response approach and iii) the dimensional approach and proposed a three continuous dimensional representation model for emotions. The clustering experiment on the given valence, arousal and dominance values of various emotions has been done to validate the proposed model. A novel approach for multimodal fusion of information from a large number of channels to classify and predict emotions has also been proposed. Discrete Wavelet Transform, a classical transform for multiresolution analysis of signal has been used in this study. The experiments are performed to classify different emotions from four classifiers. The average accuracies are 81.45%, 74.37%, 57.74% and 75.94% for SVM, MLP, KNN and MMC classifiers respectively. The best accuracy is for 'Depressing' with 85.46% using SVM. The 32 EEG channels are considered as independent modes and features from each channel are considered with equal importance. May be some of the channel data are correlated but they may contain supplementary information. In comparison with the results given by others, the high accuracy of 85% with 13 emotions and 32 subjects from our proposed method clearly proves the potential of our multimodal fusion approach. Copyright © 2013 Elsevier Inc. All rights reserved.

On the Efficiency of Individualized Theta/Beta Ratio Neurofeedback Combined with Forehead EMG Training in ADHD Children

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Olga M. Bazanova

2018-01-01

Full Text Available Background: Neurofeedback training (NFT to decrease the theta/beta ratio (TBR has been used for treating hyperactivity and impulsivity in attention deficit hyperactivity disorder (ADHD; however, often with low efficiency. Individual variance in EEG profile can confound NFT, because it may lead to influencing non-relevant activity, if ignored. More importantly, it may lead to influencing ADHD-related activities adversely, which may even result in worsening ADHD symptoms. Electromyogenic (EMG signal resulted from forehead muscles can also explain the low efficiency of the NFT in ADHD from both practical and psychological point-of-view. The first aim of this study was to determine EEG and EMG biomarkers most related to the main ADHD characteristics, such as impulsivity and hyperactivity. The second aim was to confirm our hypothesis that the efficiency of the TBR NFT can be increased by individual adjustment of the frequency bands and simultaneous training on forehead muscle tension.Methods: We recruited 94 children diagnosed with ADHD (ADHD and 23 healthy controls (HC. All participants were male and aged between six and nine. Impulsivity and attention were assessed with Go/no-Go task and delayed gratification task, respectively; and 19-channel EEG and forehead EMG were recorded. Then, the ADHD group was randomly subdivided into (1 standard, (2 individualized, (3 individualized+EMG, and (4 sham NFT (control groups. The groups were compared based on TBR and EEG alpha activity, as well as hyperactivity and impulsivity three times: pre-NFT, post-NFT and 6 months after the NFT (follow-up.Results: ADHD children were characterized with decreased individual alpha peak frequency, alpha bandwidth and alpha amplitude suppression magnitude, as well as with increased alpha1/alpha2 (a1/a2 ratio and scalp muscle tension when c (η2 ≥ 0.212. All contingent TBR NFT groups exhibited significant NFT-related decrease in TBR not evident in the control group. Moreover

On the Efficiency of Individualized Theta/Beta Ratio Neurofeedback Combined with Forehead EMG Training in ADHD Children.

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Bazanova, Olga M; Auer, Tibor; Sapina, Elena A

2018-01-01

Background: Neurofeedback training (NFT) to decrease the theta/beta ratio (TBR) has been used for treating hyperactivity and impulsivity in attention deficit hyperactivity disorder (ADHD); however, often with low efficiency. Individual variance in EEG profile can confound NFT, because it may lead to influencing non-relevant activity, if ignored. More importantly, it may lead to influencing ADHD-related activities adversely, which may even result in worsening ADHD symptoms. Electromyogenic (EMG) signal resulted from forehead muscles can also explain the low efficiency of the NFT in ADHD from both practical and psychological point-of-view. The first aim of this study was to determine EEG and EMG biomarkers most related to the main ADHD characteristics, such as impulsivity and hyperactivity. The second aim was to confirm our hypothesis that the efficiency of the TBR NFT can be increased by individual adjustment of the frequency bands and simultaneous training on forehead muscle tension. Methods: We recruited 94 children diagnosed with ADHD (ADHD) and 23 healthy controls (HC). All participants were male and aged between six and nine. Impulsivity and attention were assessed with Go/no-Go task and delayed gratification task, respectively; and 19-channel EEG and forehead EMG were recorded. Then, the ADHD group was randomly subdivided into (1) standard, (2) individualized, (3) individualized+EMG, and (4) sham NFT (control) groups. The groups were compared based on TBR and EEG alpha activity, as well as hyperactivity and impulsivity three times: pre-NFT, post-NFT and 6 months after the NFT (follow-up). Results: ADHD children were characterized with decreased individual alpha peak frequency, alpha bandwidth and alpha amplitude suppression magnitude, as well as with increased alpha1/alpha2 (a1/a2) ratio and scalp muscle tension when c (η 2 ≥ 0.212). All contingent TBR NFT groups exhibited significant NFT-related decrease in TBR not evident in the control group. Moreover

Natural mediotrusive contact: does it affect the masticatory and neck EMG activity during tooth grinding?

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Fuentes, Aler D; Martin, Conchita; Bull, Ricardo; Santander, Hugo; Gutiérrez, Mario F; Miralles, Rodolfo

2016-07-01

There is scarce knowledge regarding the influence of a natural mediotrusive contact on mandibular and cervical muscular activity. The purpose of this study was to analyze the EMG activity of the anterior temporalis (AT) and sternocleidomastoid (SCM) muscles during awake grinding in healthy subjects with or without a natural mediotrusive occlusal contact. Fifteen subjects with natural mediotrusive occlusal contact (Group 1) and 15 subjects without natural mediotrusive occlusal contact (Group 2) participated. Bilateral surface EMG activity of AT and SCM muscles was recorded during unilateral eccentric or concentric tooth grinding tasks. EMG activity was normalized against the activity recorded during maximal voluntary clenching in intercuspal position (IP) for AT muscles and during maximal intentional isometric head-neck rotation to each side, for SCM muscles. EMG activity of AT and SCM muscles showed no statistical difference between groups. EMG activity of AT muscle was higher in the working side (WS) than in the non-WS (NWS) in Group 1 during concentric grinding (0.492 vs 0.331, p = 0.047), whereas no difference was observed in Group 2. EMG activity of SCM was similar between working and NWSs in both groups and tasks. Asymmetry indexes (AIs) were not significantly different between groups. These findings in healthy subjects support the assumption that during awake tooth grinding, central nerve control predominates over peripheral inputs, and reinforce the idea of a functional link between the motor-neuron pools that control jaw and neck muscles.

Age related neuromuscular changes in sEMG of m. Tibialis Anterior using higher order statistics (Gaussianity & linearity test).

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Siddiqi, Ariba; Arjunan, Sridhar P; Kumar, Dinesh K

2016-08-01

Age-associated changes in the surface electromyogram (sEMG) of Tibialis Anterior (TA) muscle can be attributable to neuromuscular alterations that precede strength loss. We have used our sEMG model of the Tibialis Anterior to interpret the age-related changes and compared with the experimental sEMG. Eighteen young (20-30 years) and 18 older (60-85 years) performed isometric dorsiflexion at 6 different percentage levels of maximum voluntary contractions (MVC), and their sEMG from the TA muscle was recorded. Six different age-related changes in the neuromuscular system were simulated using the sEMG model at the same MVCs as the experiment. The maximal power of the spectrum, Gaussianity and Linearity Test Statistics were computed from the simulated and experimental sEMG. A correlation analysis at α=0.05 was performed between the simulated and experimental age-related change in the sEMG features. The results show the loss in motor units was distinguished by the Gaussianity and Linearity test statistics; while the maximal power of the PSD distinguished between the muscular factors. The simulated condition of 40% loss of motor units with halved the number of fast fibers best correlated with the age-related change observed in the experimental sEMG higher order statistical features. The simulated aging condition found by this study corresponds with the moderate motor unit remodelling and negligible strength loss reported in literature for the cohorts aged 60-70 years.

Seizure Classification From EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System.

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Jiang, Yizhang; Wu, Dongrui; Deng, Zhaohong; Qian, Pengjiang; Wang, Jun; Wang, Guanjin; Chung, Fu-Lai; Choi, Kup-Sze; Wang, Shitong

2017-12-01

Recognition of epileptic seizures from offline EEG signals is very important in clinical diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine learning approaches can be faster and more consistent. However, the classification accuracy is usually not satisfactory for two main reasons: the distributions of the data used for training and testing may be different, and the amount of training data may not be enough. In addition, most machine learning approaches generate black-box models that are difficult to interpret. In this paper, we integrate transductive transfer learning, semi-supervised learning and TSK fuzzy system to tackle these three problems. More specifically, we use transfer learning to reduce the discrepancy in data distribution between the training and testing data, employ semi-supervised learning to use the unlabeled testing data to remedy the shortage of training data, and adopt TSK fuzzy system to increase model interpretability. Two learning algorithms are proposed to train the system. Our experimental results show that the proposed approaches can achieve better performance than many state-of-the-art seizure classification algorithms.

Removing ECG Artifact from the Surface EMG Signal Using Adaptive Subtraction Technique

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Abbaspour, S; Fallah, A

2014-01-01

Background: The electrocardiogram artifact is a major contamination in the electromyogram signals when electromyogram signal is recorded from upper trunk muscles and because of that the contaminated electromyogram is not useful. Objective: Removing electrocardiogram contamination from electromyogram signals. Methods: In this paper, the clean electromyogram signal, electrocardiogram artifact and electrocardiogram signal were recorded from leg muscles, the pectoralis major muscle of the left side and V4, respectively. After the pre-processing, contaminated electromyogram signal is simulated with a combination of clean electromyogram and electrocardiogram artifact. Then, contaminated electromyogram is cleaned using adaptive subtraction method. This method contains some steps; (1) QRS detection, (2) formation of electrocardiogram template by averaging the electrocardiogram complexes, (3) using low pass filter to remove undesirable artifacts, (4) subtraction. Results: Performance of our method is evaluated using qualitative criteria, power spectrum density and coherence and quantitative criteria signal to noise ratio, relative error and cross correlation. The result of signal to noise ratio, relative error and cross correlation is equal to 10.493, 0.04 and %97 respectively. Finally, there is a comparison between proposed method and some existing methods. Conclusion: The result indicates that adaptive subtraction method is somewhat effective to remove electrocardiogram artifact from contaminated electromyogram signal and has an acceptable result. PMID:25505766

EEG signal classification using PSO trained RBF neural network for epilepsy identification

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Sandeep Kumar Satapathy

Full Text Available The electroencephalogram (EEG is a low amplitude signal generated in the brain, as a result of information flow during the communication of several neurons. Hence, careful analysis of these signals could be useful in understanding many human brain disorder diseases. One such disease topic is epileptic seizure identification, which can be identified via a classification process of the EEG signal after preprocessing with the discrete wavelet transform (DWT. To classify the EEG signal, we used a radial basis function neural network (RBFNN. As shown herein, the network can be trained to optimize the mean square error (MSE by using a modified particle swarm optimization (PSO algorithm. The key idea behind the modification of PSO is to introduce a method to overcome the problem of slow searching in and around the global optimum solution. The effectiveness of this procedure was verified by an experimental analysis on a benchmark dataset which is publicly available. The result of our experimental analysis revealed that the improvement in the algorithm is significant with respect to RBF trained by gradient descent and canonical PSO. Here, two classes of EEG signals were considered: the first being an epileptic and the other being non-epileptic. The proposed method produced a maximum accuracy of 99% as compared to the other techniques. Keywords: Electroencephalography, Radial basis function neural network, Particle swarm optimization, Discrete wavelet transform, Machine learning

Real Time Hand Motion Reconstruction System for Trans-Humeral Amputees Using EEG and EMG

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Jacobo Fernandez-Vargas

2016-08-01

Full Text Available Predicting a hand’s position using only biosignals is a complex problem that has not been completely solved. The only reliable solutions currently available require invasive surgery. The attempts using non-invasive technologies are rare, and usually have led to lower correlation values between the real and the reconstructed position than those required for real-world applications. In this study, we propose a solution for reconstructing the hand’s position in three dimensions using EEG and EMG to detect from the shoulder area. This approach would be valid for most trans-humeral amputees. In order to find the best solution, we tested four different architectures for the system based on artificial neural networks. Our results show that it is possible to reconstruct the hand’s motion trajectory with a correlation value up to 0.809 compared to a typical value in the literature of 0.6. We also demonstrated that both EEG and EMG contribute jointly to the motion reconstruction. Furthermore, we discovered that the system architectures do not change the results radically. In addition, our results suggest that different motions may have different brain activity patterns that could be detected through EEG. Finally, we suggest a method to study non-linear relations in the brain through the EEG signals, which may lead to a more accurate system.

Analysis of Muscle Fatigue Progression using Cyclostationary Property of Surface Electromyography Signals.

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Karthick, P A; Venugopal, G; Ramakrishnan, S

2016-01-01

Analysis of neuromuscular fatigue finds various applications ranging from clinical studies to biomechanics. Surface electromyography (sEMG) signals are widely used for these studies due to its non-invasiveness. During cyclic dynamic contractions, these signals are nonstationary and cyclostationary. In recent years, several nonstationary methods have been employed for the muscle fatigue analysis. However, cyclostationary based approach is not well established for the assessment of muscle fatigue. In this work, cyclostationarity associated with the biceps brachii muscle fatigue progression is analyzed using sEMG signals and Spectral Correlation Density (SCD) functions. Signals are recorded from fifty healthy adult volunteers during dynamic contractions under a prescribed protocol. These signals are preprocessed and are divided into three segments, namely, non-fatigue, first muscle discomfort and fatigue zones. Then SCD is estimated using fast Fourier transform accumulation method. Further, Cyclic Frequency Spectral Density (CFSD) is calculated from the SCD spectrum. Two features, namely, cyclic frequency spectral area (CFSA) and cyclic frequency spectral entropy (CFSE) are proposed to study the progression of muscle fatigue. Additionally, degree of cyclostationarity (DCS) is computed to quantify the amount of cyclostationarity present in the signals. Results show that there is a progressive increase in cyclostationary during the progression of muscle fatigue. CFSA shows an increasing trend in muscle fatiguing contraction. However, CFSE shows a decreasing trend. It is observed that when the muscle progresses from non-fatigue to fatigue condition, the mean DCS of fifty subjects increases from 0.016 to 0.99. All the extracted features found to be distinct and statistically significant in the three zones of muscle contraction (p < 0.05). It appears that these SCD features could be useful in the automated analysis of sEMG signals for different neuromuscular conditions.

Decoding subtle forearm flexions using fractal features of surface electromyogram from single and multiple sensors.

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Arjunan, Sridhar Poosapadi; Kumar, Dinesh Kant

2010-10-21

Identifying finger and wrist flexion based actions using a single channel surface electromyogram (sEMG) can lead to a number of applications such as sEMG based controllers for near elbow amputees, human computer interface (HCI) devices for elderly and for defence personnel. These are currently infeasible because classification of sEMG is unreliable when the level of muscle contraction is low and there are multiple active muscles. The presence of noise and cross-talk from closely located and simultaneously active muscles is exaggerated when muscles are weakly active such as during sustained wrist and finger flexion. This paper reports the use of fractal properties of sEMG to reliably identify individual wrist and finger flexion, overcoming the earlier shortcomings. SEMG signal was recorded when the participant maintained pre-specified wrist and finger flexion movements for a period of time. Various established sEMG signal parameters such as root mean square (RMS), Mean absolute value (MAV), Variance (VAR) and Waveform length (WL) and the proposed fractal features: fractal dimension (FD) and maximum fractal length (MFL) were computed. Multi-variant analysis of variance (MANOVA) was conducted to determine the p value, indicative of the significance of the relationships between each of these parameters with the wrist and finger flexions. Classification accuracy was also computed using the trained artificial neural network (ANN) classifier to decode the desired subtle movements. The results indicate that the p value for the proposed feature set consisting of FD and MFL of single channel sEMG was 0.0001 while that of various combinations of the five established features ranged between 0.009 - 0.0172. From the accuracy of classification by the ANN, the average accuracy in identifying the wrist and finger flexions using the proposed feature set of single channel sEMG was 90%, while the average accuracy when using a combination of other features ranged between 58% and 73

Decoding subtle forearm flexions using fractal features of surface electromyogram from single and multiple sensors

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Kumar Dinesh

2010-10-01

Full Text Available Abstract Background Identifying finger and wrist flexion based actions using a single channel surface electromyogram (sEMG can lead to a number of applications such as sEMG based controllers for near elbow amputees, human computer interface (HCI devices for elderly and for defence personnel. These are currently infeasible because classification of sEMG is unreliable when the level of muscle contraction is low and there are multiple active muscles. The presence of noise and cross-talk from closely located and simultaneously active muscles is exaggerated when muscles are weakly active such as during sustained wrist and finger flexion. This paper reports the use of fractal properties of sEMG to reliably identify individual wrist and finger flexion, overcoming the earlier shortcomings. Methods SEMG signal was recorded when the participant maintained pre-specified wrist and finger flexion movements for a period of time. Various established sEMG signal parameters such as root mean square (RMS, Mean absolute value (MAV, Variance (VAR and Waveform length (WL and the proposed fractal features: fractal dimension (FD and maximum fractal length (MFL were computed. Multi-variant analysis of variance (MANOVA was conducted to determine the p value, indicative of the significance of the relationships between each of these parameters with the wrist and finger flexions. Classification accuracy was also computed using the trained artificial neural network (ANN classifier to decode the desired subtle movements. Results The results indicate that the p value for the proposed feature set consisting of FD and MFL of single channel sEMG was 0.0001 while that of various combinations of the five established features ranged between 0.009 - 0.0172. From the accuracy of classification by the ANN, the average accuracy in identifying the wrist and finger flexions using the proposed feature set of single channel sEMG was 90%, while the average accuracy when using a combination

Relationship among the myelography, MRI and EMG in young patients with low back pain or radiating pain

International Nuclear Information System (INIS)

Jang, Ji Youn; Kim, Dong Hun; Park, Young Jae

2006-01-01

We wanted to evaluate the relationship among the myelography, magnetic resonance imaging (MRI), and electromyography (EMG) findings in young patients with low back pain, and we wanted to assess the significance of the spinal geometric measurements as well as type of disc herniation seen on MRI. Forty-four young men with lower back pain were included, and they were all clinically suspected of suffering with lumbar disc herniation. All of them underwent myelography, MRI and EMG. We measured spinal geometry including the anteroposterior diameters of the central canal and thecal sac, the interlaminar distance, the width of the lateral recess and the thickness of the ligamentum flavum, and we evaluated for root deviation as well as disc herniation on the MRIs. We compared the types of disc herniation on MRI with the myelography and EMG findings. Also, we investigated the correlation of the spinal geometric measurements on MRI with the EMG and myelography findings. The types of disc herniation on MRI were not significantly related to the myelography (ρ = 0.298) and EMG findings (ρ = 0.372). The EMG findings were not related to either the myelography findings (ρ = 0.435) or the spinal geometric measurements (ρ > 0.05) on MRI. Nerve root compression that was noted on myelography was related to the thecal sac AP diameter (ρ = 0.016) and the width of the lateral recess (ρ = 0.011). There were no correlations between myelography and the findings of root deviation on MRI (ρ = 0.052). MRI can play an excellent diagnostic role for young patients with radiculopathy or lower back pain. It could increase the diagnostic accuracy if it is used in conjunction with myelography and EMG. The narrowing of thecal sac AP diameter and the width of lateral recess rather than the type of disc herniation on MRI were well correlated with the myelography and EMG findings

Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals.

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Peng Ren

Full Text Available Preterm delivery increases the risk of infant mortality and morbidity, and therefore developing reliable methods for predicting its likelihood are of great importance. Previous work using uterine electromyography (EMG recordings has shown that they may provide a promising and objective way for predicting risk of preterm delivery. However, to date attempts at utilizing computational approaches to achieve sufficient predictive confidence, in terms of area under the curve (AUC values, have not achieved the high discrimination accuracy that a clinical application requires. In our study, we propose a new analytical approach for assessing the risk of preterm delivery using EMG recordings which firstly employs Empirical Mode Decomposition (EMD to obtain their Intrinsic Mode Functions (IMF. Next, the entropy values of both instantaneous amplitude and instantaneous frequency of the first ten IMF components are computed in order to derive ratios of these two distinct components as features. Discrimination accuracy of this approach compared to those proposed previously was then calculated using six differently representative classifiers. Finally, three different electrode positions were analyzed for their prediction accuracy of preterm delivery in order to establish which uterine EMG recording location was optimal signal data. Overall, our results show a clear improvement in prediction accuracy of preterm delivery risk compared with previous approaches, achieving an impressive maximum AUC value of 0.986 when using signals from an electrode positioned below the navel. In sum, this provides a promising new method for analyzing uterine EMG signals to permit accurate clinical assessment of preterm delivery risk.

Influence of post-stroke spasticity on EMG-force coupling and force steadiness in biceps brachii.

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Carlyle, Jennilee K; Mochizuki, George

2018-02-01

Individuals with spasticity after stroke experience a decrease in force steadiness which can impact function. Alterations in the strength of EMG-force coupling may contribute to the reduction in force steadiness observed in spasticity. The aim was to determine the extent to which force steadiness and EMG-force coupling is affected by post-stroke spasticity. This cross-sectional study involved individuals with upper limb spasticity after stroke. Participants were required to generate and maintain isometric contractions of the elbow flexors at varying force levels. Coefficient of variation of force, absolute force, EMG-force cross-correlation function peak and peak latency was measured from both limbs with surface electromyography and isometric dynamometry. Statistically significant differences were observed between the affected and less affected limbs for all outcome measures. Significant main effects of force level were also observed. Force steadiness was not statistically significantly correlated with EMG-force coupling; however, both force steadiness and absolute force were associated with the level of impairment as measured by the Chedoke McMaster Stroke Assessment Scale. Spasticity after stroke uncouples the relationship between EMG and force and is associated with reduced force steadiness during isometric contractions; however, these features of control are not associated in individuals with spasticity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Generalized approach to bilateral control for EMG driven exoskeleton

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Gradetsky Valery

2017-01-01

Full Text Available The paper discusses a generalized approach to bilateral control for EMG driven exoskeleton systems. In this paper we consider a semi-automatic mechatronic system that is controlled via human muscle activity (EMG level. The problem is to understand how the movement of the exoskeleton effects on the control. The considered system can be described in terms of bilateral control. This means the existence of force feedback from the object via the exoskeleton links and drives to operator. The simulation of the considered model was held on the MATLAB Simulink. The mathematical model of the bilateral system with exoskeleton and operator was developed. Transient functions for different dynamic parameters were obtained. It was shown that force feedback is essential for the R&D of such systems.

The reliability of surface EMG recorded from the pelvic floor muscles.

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Auchincloss, Cindy C; McLean, Linda

2009-08-30

The neuromuscular function of the pelvic floor muscles (PFMs) is frequently evaluated using surface electrodes embedded on vaginal probes. The purpose of this study was to determine the between-trial and between-day reliability of EMG data recorded from the PFM using two different vaginal probes while subjects performed PFM maximum voluntary contractions and a coughing task. The Femiscan and the Periform vaginal probes were used to acquire EMG data while the subjects performed the tasks. Peak RMS amplitudes were computed for each instrument, task, and side of the pelvic floor using a sliding window technique. The between-trial reliability was evaluated using intraclass correlation coefficients (ICCs) and coefficients of variation (CV). Between-trial reliability was determined using ICCs, Pearson's correlation coefficients, computing the mean absolute difference between days, and calculating the standard error the measurement (SEM) for each instrument and task. EMG amplitude differences were detected between the left and right PFM (pperformed separately for each side. Overall, between-trial reliability was fair to high for the Femiscan (ICC((3,1))=0.58-0.98, CV=8.5-20.7%) and good to high for the Periform (ICC((3,1))=0.80-0.98, CV=9.6-19.5%), however between-day reliability was generally poor for both vaginal probes (ICC((3,1))=0.08-0.84). The results suggest that although it is acceptable to use PFM surface EMG as a biofeedback tool for training purposes, it is not recommended for use to make between-subject comparisons or to use as an outcome measure between-days when evaluating PFM function.

A comparative study of efficacy of emg bio-feedback and progressive muscular relaxation in tension headache.

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Gada, M T

1984-04-01

The aim of the present study was to find out efficacy of frontalis EMG Biofeedback therapy, deep muscular relaxation therapy and compare the efficacy of both in cases of tension headache. During two week basal-data recording period all patients were taught deep muscular relaxation by Jacobson's technique. Simultaneously patients were instructed to keep headache diary. Headache diary yielded three different parameters a) number of headache-free days per week, b) peak headache intensity (or each week and c) average daily headache activity score per week. These parameters were used to find out therapeutic efficacy of each treatment. Patients were randomly divided in two groups. EMG Biofeedback group was given frontalis EMG feedback through EMG J 33 muscle trainer of Cyborg Corporation (U.S.A.). Patients in each group were given 20 sessions (two sessions per week); each session lasting 30 minutes. Patients were instructed to practice at least one 30 minute session of relaxation at home. The data were subjected to statistical calculation. The results indicate that frontalis EMG Biofeedback therapy and deep muscle relaxation therapy are significantly effective in cases of tension headache. Both treatments are equally effective. The findings are discussed in relation to Indian situation.

Viability of Controlling Prosthetic Hand Utilizing Electroencephalograph (EEG) Dataset Signal

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Miskon, Azizi; A/L Thanakodi, Suresh; Raihan Mazlan, Mohd; Mohd Haziq Azhar, Satria; Nooraya Mohd Tawil, Siti

2016-11-01

This project presents the development of an artificial hand controlled by Electroencephalograph (EEG) signal datasets for the prosthetic application. The EEG signal datasets were used as to improvise the way to control the prosthetic hand compared to the Electromyograph (EMG). The EMG has disadvantages to a person, who has not used the muscle for a long time and also to person with degenerative issues due to age factor. Thus, the EEG datasets found to be an alternative for EMG. The datasets used in this work were taken from Brain Computer Interface (BCI) Project. The datasets were already classified for open, close and combined movement operations. It served the purpose as an input to control the prosthetic hand by using an Interface system between Microsoft Visual Studio and Arduino. The obtained results reveal the prosthetic hand to be more efficient and faster in response to the EEG datasets with an additional LiPo (Lithium Polymer) battery attached to the prosthetic. Some limitations were also identified in terms of the hand movements, weight of the prosthetic, and the suggestions to improve were concluded in this paper. Overall, the objective of this paper were achieved when the prosthetic hand found to be feasible in operation utilizing the EEG datasets.

Phase Clustering Based Modulation Classification Algorithm for PSK Signal over Wireless Environment

Directory of Open Access Journals (Sweden)

Qi An

2016-01-01

Full Text Available Promptitude and accuracy of signals’ non-data-aided (NDA identification is one of the key technology demands in noncooperative wireless communication network, especially in information monitoring and other electronic warfare. Based on this background, this paper proposes a new signal classifier for phase shift keying (PSK signals. The periodicity of signal’s phase is utilized as the assorted character, with which a fractional function is constituted for phase clustering. Classification and the modulation order of intercepted signals can be achieved through its Fast Fourier Transform (FFT of the phase clustering function. Frequency offset is also considered for practical conditions. The accuracy of frequency offset estimation has a direct impact on its correction. Thus, a feasible solution is supplied. In this paper, an advanced estimator is proposed for estimating the frequency offset and balancing estimation accuracy and range under low signal-to-noise ratio (SNR conditions. The influence on estimation range brought by the maximum correlation interval is removed through the differential operation of the autocorrelation of the normalized baseband signal raised to the power of Q. Then, a weighted summation is adopted for an effective frequency estimation. Details of equations and relevant simulations are subsequently presented. The estimator proposed can reach an estimation accuracy of 10-4 even when the SNR is as low as -15 dB. Analytical formulas are expressed, and the corresponding simulations illustrate that the classifier proposed is more efficient than its counterparts even at low SNRs.

Detection of geodesic acoustic mode oscillations, using multiple signal classification analysis of Doppler backscattering signal on Tore Supra

International Nuclear Information System (INIS)

Vermare, L.; Hennequin, P.; Gürcan, Ö.D.

2012-01-01

This paper presents the first observation of geodesic acoustic modes (GAMs) on Tore Supra plasmas. Using the Doppler backscattering system, the oscillations of the plasma flow velocity, localized between r/a = 0.85 and r/a = 0.95, and with a frequency, typically around 10 kHz, have been observed at the plasma edge in numerous discharges. When the additional heating power is varied, the frequency is found to scale with C s /R. The MUltiple SIgnal Classification (MUSIC) algorithm is employed to access the temporal evolution of the perpendicular velocity of density fluctuations. The method is presented in some detail, and is validated and compared against standard methods, such as the conventional fast Fourier transform method, using a synthetic signal. It stands out as a powerful data analysis method to follow the Doppler frequency with a high temporal resolution, which is important in order to extract the dynamics of GAMs. (paper)

3D-printing soft sEMG sensing structures

NARCIS (Netherlands)

Wolterink, Gerjan; Sanders, Remco; Muijzer, Frodo; van Beijnum, Bert-Jan; Krijnen, Gijs

2017-01-01

This paper describes the development and characterization of soft and flexible 3D-printed sEMG electrodes. The electrodes are printed in one go on a low cost consumer multi-material FDM printer. The printed structures do not need any further production steps to give them conductive properties.

Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis

Science.gov (United States)

Javed, Amna; Tiwana, Mohsin I.; Khan, Umar Shahbaz

2018-01-01

Brain Computer Interface (BCI) determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG) signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement). Two-stage logistic regression classifier exhibited the highest classification accuracy while Power Spectral Density (PSD) was used as a feature of the filtered signal. The EEG signal data set was recorded using a 14-channel electrode headset (a noninvasive BCI system) from right-handed, neurologically intact volunteers. Mu (commonly known as alpha waves) and Beta Rhythms (8–30 Hz) containing most of the movement data were retained through filtering using “Arduino Uno” microcontroller followed by 2-stage logistic regression to obtain a mean classification accuracy of 70%. PMID:29888252

Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis

Directory of Open Access Journals (Sweden)

Nasir Rashid

2018-01-01

Full Text Available Brain Computer Interface (BCI determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement. Two-stage logistic regression classifier exhibited the highest classification accuracy while Power Spectral Density (PSD was used as a feature of the filtered signal. The EEG signal data set was recorded using a 14-channel electrode headset (a noninvasive BCI system from right-handed, neurologically intact volunteers. Mu (commonly known as alpha waves and Beta Rhythms (8–30 Hz containing most of the movement data were retained through filtering using “Arduino Uno” microcontroller followed by 2-stage logistic regression to obtain a mean classification accuracy of 70%.

Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis.

Science.gov (United States)

Rashid, Nasir; Iqbal, Javaid; Javed, Amna; Tiwana, Mohsin I; Khan, Umar Shahbaz

2018-01-01

Brain Computer Interface (BCI) determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG) signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement). Two-stage logistic regression classifier exhibited the highest classification accuracy while Power Spectral Density (PSD) was used as a feature of the filtered signal. The EEG signal data set was recorded using a 14-channel electrode headset (a noninvasive BCI system) from right-handed, neurologically intact volunteers. Mu (commonly known as alpha waves) and Beta Rhythms (8-30 Hz) containing most of the movement data were retained through filtering using "Arduino Uno" microcontroller followed by 2-stage logistic regression to obtain a mean classification accuracy of 70%.

Classification of EEG-P300 Signals Extracted from Brain Activities in BCI Systems Using ν-SVM and BLDA Algorithms

Directory of Open Access Journals (Sweden)

Ali MOMENNEZHAD

2014-06-01

Full Text Available In this paper, a linear predictive coding (LPC model is used to improve classification accuracy, convergent speed to maximum accuracy, and maximum bitrates in brain computer interface (BCI system based on extracting EEG-P300 signals. First, EEG signal is filtered in order to eliminate high frequency noise. Then, the parameters of filtered EEG signal are extracted using LPC model. Finally, the samples are reconstructed by LPC coefficients and two classifiers, a Bayesian Linear discriminant analysis (BLDA, and b the υ-support vector machine (υ-SVM are applied in order to classify. The proposed algorithm performance is compared with fisher linear discriminant analysis (FLDA. Results show that the efficiency of our algorithm in improving classification accuracy and convergent speed to maximum accuracy are much better. As example at the proposed algorithms, respectively BLDA with LPC model and υ-SVM with LPC model with8 electrode configuration for subject S1 the total classification accuracy is improved as 9.4% and 1.7%. And also, subject 7 at BLDA and υ-SVM with LPC model algorithms (LPC+BLDA and LPC+ υ-SVM after block 11th converged to maximum accuracy but Fisher Linear Discriminant Analysis (FLDA algorithm did not converge to maximum accuracy (with the same configuration. So, it can be used as a promising tool in designing BCI systems.

Effects of the innervation zone on the time and frequency domain parameters of the surface electromyographic signal.

Science.gov (United States)

Smith, Cory M; Housh, Terry J; Herda, Trent J; Zuniga, Jorge M; Ryan, Eric D; Camic, Clayton L; Bergstrom, Haley C; Smith, Doug B; Weir, Joseph P; Cramer, Joel T; Hill, Ethan C; Cochrane, Kristen C; Jenkins, Nathaniel D M; Schmidt, Richard J; Johnson, Glen O

2015-08-01

The purposes of the present study were to examine the effects of electrode placements over, proximal, and distal to the innervation zone (IZ) on electromyographic (EMG) amplitude (RMS) and frequency (MPF) responses during: (1) a maximal voluntary isometric contraction (MVIC), and; (2) a sustained, submaximal isometric muscle action. A linear array was used to record EMG signals from the vastus lateralis over the IZ, 30mm proximal, and 30mm distal to the IZ during an MVIC and a sustained isometric muscle action of the leg extensors at 50% MVIC. During the MVIC, lower EMG RMS (p>0.05) and greater EMG MPF (ptime relationships over, proximal, and distal to the IZ occurred. Thus, the results of the present study indicated that during an MVIC, EMG RMS and MPF values recorded over the IZ are not comparable to those away from the IZ. However, the rates of fatigue-induced changes in EMG RMS and MPF during sustained, submaximal isometric muscle actions of the leg extensors were the same regardless of the electrode placement locations relative to the IZ. Copyright © 2015 Elsevier Ltd. All rights reserved.

Radar transmitter classification using non-stationary signal classifier

CSIR Research Space (South Africa)

Du Plessis, MC

2009-07-01

Full Text Available support vector machine which is applied to the radar pulse's time-frequency representation. The time-frequency representation is refined using particle swarm optimization to increase the classification accuracy. The classification accuracy is tested...

Fusion of ECG and ABP signals based on wavelet transform for cardiac arrhythmias classification.

Science.gov (United States)

Arvanaghi, Roghayyeh; Daneshvar, Sabalan; Seyedarabi, Hadi; Goshvarpour, Atefeh

2017-11-01

Each of Electrocardiogram (ECG) and Atrial Blood Pressure (ABP) signals contain information of cardiac status. This information can be used for diagnosis and monitoring of diseases. The majority of previously proposed methods rely only on ECG signal to classify heart rhythms. In this paper, ECG and ABP were used to classify five different types of heart rhythms. To this end, two mentioned signals (ECG and ABP) have been fused. These physiological signals have been used from MINIC physioNet database. ECG and ABP signals have been fused together on the basis of the proposed Discrete Wavelet Transformation fusion technique. Then, some frequency features were extracted from the fused signal. To classify the different types of cardiac arrhythmias, these features were given to a multi-layer perceptron neural network. In this study, the best results for the proposed fusion algorithm were obtained. In this case, the accuracy rates of 96.6%, 96.9%, 95.6% and 93.9% were achieved for two, three, four and five classes, respectively. However, the maximum classification rate of 89% was obtained for two classes on the basis of ECG features. It has been found that the higher accuracy rates were acquired by using the proposed fusion technique. The results confirmed the importance of fusing features from different physiological signals to gain more accurate assessments. Copyright © 2017 Elsevier B.V. All rights reserved.

Suppression of EMG activity by transcranial magnetic stimulation in human subjects during walking

DEFF Research Database (Denmark)

Petersen, Nicolas Caesar; Butler, Jane E; Marchand-Pauvert, Veronique

2001-01-01

1. The involvement of the motor cortex during human walking was evaluated using transcranial magnetic stimulation (TMS) of the motor cortex at a variety of intensities. Recordings of EMG activity in tibialis anterior (TA) and soleus muscles during walking were rectified and averaged. 2. TMS of low...... intensity (below threshold for a motor-evoked potential, MEP) produced a suppression of ongoing EMG activity during walking. The average latency for this suppression was 40.0 +/- 1.0 ms. At slightly higher intensities of stimulation there was a facilitation of the EMG activity with an average latency of 29.......5 +/- 1.0 ms. As the intensity of the stimulation was increased the facilitation increased in size and eventually a MEP was clear in individual sweeps. 3. In three subjects TMS was replaced by electrical stimulation over the motor cortex. Just below MEP threshold there was a clear facilitation at short...

Contributions to muscle force and EMG by combined neural excitation and electrical stimulation

Science.gov (United States)

Crago, Patrick E.; Makowski, Nathaniel S.; Cole, Natalie M.

2014-10-01

Objective. Stimulation of muscle for research or clinical interventions is often superimposed on ongoing physiological activity without a quantitative understanding of the impact of the stimulation on the net muscle activity and the physiological response. Experimental studies show that total force during stimulation is less than the sum of the isolated voluntary and stimulated forces, but the occlusion mechanism is not understood. Approach. We develop a model of efferent motor activity elicited by superimposing stimulation during a physiologically activated contraction. The model combines action potential interactions due to collision block, source resetting, and refractory periods with previously published models of physiological motor unit recruitment, rate modulation, force production, and EMG generation in human first dorsal interosseous muscle to investigate the mechanisms and effectiveness of stimulation on the net muscle force and EMG. Main results. Stimulation during a physiological contraction demonstrates partial occlusion of force and the neural component of the EMG, due to action potential interactions in motor units activated by both sources. Depending on neural and stimulation firing rates as well as on force-frequency properties, individual motor unit forces can be greater, smaller, or unchanged by the stimulation. In contrast, voluntary motor unit EMG potentials in simultaneously stimulated motor units show progressive occlusion with increasing stimulus rate. The simulations predict that occlusion would be decreased by a reverse stimulation recruitment order. Significance. The results are consistent with and provide a mechanistic interpretation of previously published experimental evidence of force occlusion. The models also predict two effects that have not been reported previously—voluntary EMG occlusion and the advantages of a proximal stimulation site. This study provides a basis for the rational design of both future experiments and clinical

Pitch Based Sound Classification

DEFF Research Database (Denmark)

Nielsen, Andreas Brinch; Hansen, Lars Kai; Kjems, U

2006-01-01

A sound classification model is presented that can classify signals into music, noise and speech. The model extracts the pitch of the signal using the harmonic product spectrum. Based on the pitch estimate and a pitch error measure, features are created and used in a probabilistic model with soft......-max output function. Both linear and quadratic inputs are used. The model is trained on 2 hours of sound and tested on publicly available data. A test classification error below 0.05 with 1 s classification windows is achieved. Further more it is shown that linear input performs as well as a quadratic......, and that even though classification gets marginally better, not much is achieved by increasing the window size beyond 1 s....

Identificación de Tareas Isométricas y Dinámicas del Miembro Superior Basada en EMG de Alta Densidad

Directory of Open Access Journals (Sweden)

Mónica Rojas- Martínez

2017-10-01

robots used in active rehabilitation processes. The emerging technology of high-density electromyography (HD-EMG opens up new possibilities to extract neural information, and it has already been reported that the spatial distribution of HD-EMG intensity maps is a valuable feature in the identification of isometric tasks.This study explores the use of the spatial distribution of myoelectric activity and carries out a task identification during dynamic exercises at different velocities which are much closer to the ones commonly used during therapy. To this end, HD-EMG signals were recorded in a group of healthy subjects while performing a set of isometric and dynamic upper limb tasks. The results show that spatial distribution is a very useful feature in the identification not only of isometric contractions but also of dynamic contractions, so it can be very useful to improve the control of rehabilitation devices, making it more natural and permitting to adapt better to the user. Palabras clave: Bioingeniería, electromiografía, neuromuscular, rehabilitación, Keywords: Bioengineering, electromiography, neuromuscular, rehabilitation

Augmented effects of EMG biofeedback interfaced with virtual reality on neuromuscular control and movement coordination during reaching in children with cerebral palsy.

Science.gov (United States)

Yoo, Ji Won; Lee, Dong Ryul; Cha, Young Joo; You, Sung Hyun

2017-01-01

The purpose of the present study was to compare therapeutic effects of an electromyography (EMG) biofeedback augmented by virtual reality (VR) and EMG biofeedback alone on the triceps and biceps (T:B) muscle activity imbalance and elbow joint movement coordination during a reaching motor taskOBJECTIVE: To compare therapeutic effects of an electromyography (EMG) biofeedback augmented by virtual reality (VR) and EMG biofeedback alone on the triceps and biceps muscle activity imbalance and elbow joint movement coordination during a reaching motor task in normal children and children with spastic cerebral palsy (CP). 18 children with spastic CP (2 females; mean±standard deviation = 9.5 ± 1.96 years) and 8 normal children (3 females; mean ± standard deviation = 9.75 ± 2.55 years) were recruited from a local community center. All children with CP first underwent one intensive session of EMG feedback (30 minutes), followed by one session of the EMG-VR feedback (30 minutes) after a 1-week washout period. Clinical tests included elbow extension range of motion (ROM), biceps muscle strength, and box and block test. EMG triceps and biceps (T:B) muscle activity imbalance and reaching movement acceleration coordination were concurrently determined by EMG and 3-axis accelerometer measurements respectively. Independent t-test and one-way repeated analysis of variance (ANOVA) were performed at p augmented by virtual reality exercise games in children with spastic CP. The augmented EMG and VR feedback produced better neuromuscular balance control in the elbow joint than the EMG biofeedback alone.

Use of electromyographic and electrocardiographic signals to detect sleep bruxism episodes in a natural environment.

Science.gov (United States)

Castroflorio, Tommaso; Mesin, Luca; Tartaglia, Gianluca Martino; Sforza, Chiarella; Farina, Dario

2013-11-01

Diagnosis of bruxism is difficult since not all contractions of masticatory muscles during sleeping are bruxism episodes. In this paper, we propose the use of both EMG and ECG signals for the detection of sleep bruxism. Data have been acquired from 21 healthy volunteers and 21 sleep bruxers. The masseter surface EMGs were detected with bipolar concentric electrodes and the ECG with monopolar electrodes located on the clavicular regions. Recordings were made at the subjects' homes during sleeping. Bruxism episodes were automatically detected as characterized by masseter EMG amplitude greater than 10% of the maximum and heart rate increasing by more than 25% with respect to baseline within 1 s before the increase in EMG amplitude above the 10% threshold. Furthermore, the subjects were classified as bruxers and nonbruxers by a neural network. The number of bruxism episodes per night was 24.6 ± 8.4 for bruxers and 4.3 ± 4.5 for controls ( P bruxism.

Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.

Science.gov (United States)

Lajnef, Tarek; Chaibi, Sahbi; Ruby, Perrine; Aguera, Pierre-Emmanuel; Eichenlaub, Jean-Baptiste; Samet, Mounir; Kachouri, Abdennaceur; Jerbi, Karim

2015-07-30

Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are promising, there is need for improvement, especially given the time-consuming and tedious nature of visual sleep scoring. Here we propose a sleep staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. The performance of the method was evaluated using polysomnographic data from 15 subjects (electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) recordings). The decision tree, or dendrogram, was obtained using a hierarchical clustering technique and a wide range of time and frequency-domain features were extracted. Feature selection was carried out using forward sequential selection and classification was evaluated using k-fold cross-validation. The dendrogram-based SVM (DSVM) achieved mean specificity, sensitivity and overall accuracy of 0.92, 0.74 and 0.88 respectively, compared to expert visual scoring. Restricting DSVM classification to data where both experts' scoring was consistent (76.73% of the data) led to a mean specificity, sensitivity and overall accuracy of 0.94, 0.82 and 0.92 respectively. The DSVM framework outperforms classification with more standard multi-class "one-against-all" SVM and linear-discriminant analysis. The promising results of the proposed methodology suggest that it may be a valuable alternative to existing automatic methods and that it could accelerate visual scoring by providing a robust starting hypnogram that can be further fine-tuned by expert inspection. Copyright © 2015 Elsevier B.V. All rights reserved.

Robust functional statistics applied to Probability Density Function shape screening of sEMG data.

Science.gov (United States)

Boudaoud, S; Rix, H; Al Harrach, M; Marin, F

2014-01-01

Recent studies pointed out possible shape modifications of the Probability Density Function (PDF) of surface electromyographical (sEMG) data according to several contexts like fatigue and muscle force increase. Following this idea, criteria have been proposed to monitor these shape modifications mainly using High Order Statistics (HOS) parameters like skewness and kurtosis. In experimental conditions, these parameters are confronted with small sample size in the estimation process. This small sample size induces errors in the estimated HOS parameters restraining real-time and precise sEMG PDF shape monitoring. Recently, a functional formalism, the Core Shape Model (CSM), has been used to analyse shape modifications of PDF curves. In this work, taking inspiration from CSM method, robust functional statistics are proposed to emulate both skewness and kurtosis behaviors. These functional statistics combine both kernel density estimation and PDF shape distances to evaluate shape modifications even in presence of small sample size. Then, the proposed statistics are tested, using Monte Carlo simulations, on both normal and Log-normal PDFs that mimic observed sEMG PDF shape behavior during muscle contraction. According to the obtained results, the functional statistics seem to be more robust than HOS parameters to small sample size effect and more accurate in sEMG PDF shape screening applications.

Muscle fatigue evaluation of astronaut upper limb based on sEMG and subjective assessment

Science.gov (United States)

Zu, Xiaoqi; Zhou, Qianxiang; Li, Yun

2012-07-01

All movements are driven by muscle contraction, and it is easy to cause muscle fatigue. Evaluation of muscle fatigue is a hot topic in the area of astronaut life support training and rehabilitation. If muscle gets into fatigue condition, it may reduce work efficiency and has an impact on psychological performance. Therefore it is necessary to develop an accurate and usable method on muscle fatigue evaluation of astronaut upper limb. In this study, we developed a method based on surface electromyography (sEMG) and subjective assessment (Borg scale) to evaluate local muscle fatigue. Fifteen healthy young male subjects participated in the experiment. They performed isometric muscle contractions of the upper limb. sEMG of the biceps brachii were recorded during the entire process of isotonic muscle contraction and Borg scales of muscle fatigue were collected in certain times. sEMG were divided into several parts, and then mean energy of each parts were calculated by the one-twelfth band octave method. Equations were derived based on the relationship between the mean energy of sEMG and Borg scale. The results showed that cubic curve could describe the degree of local muscle fatigue, and could be used to evaluate and monitor local muscle fatigue during the entire process.

Real time estimation of generation, extinction and flow of muscle fibre action potentials in high density surface EMG.

Science.gov (United States)

Mesin, Luca

2015-02-01

Developing a real time method to estimate generation, extinction and propagation of muscle fibre action potentials from bi-dimensional and high density surface electromyogram (EMG). A multi-frame generalization of an optical flow technique including a source term is considered. A model describing generation, extinction and propagation of action potentials is fit to epochs of surface EMG. The algorithm is tested on simulations of high density surface EMG (inter-electrode distance equal to 5mm) from finite length fibres generated using a multi-layer volume conductor model. The flow and source term estimated from interference EMG reflect the anatomy of the muscle, i.e. the direction of the fibres (2° of average estimation error) and the positions of innervation zone and tendons under the electrode grid (mean errors of about 1 and 2mm, respectively). The global conduction velocity of the action potentials from motor units under the detection system is also obtained from the estimated flow. The processing time is about 1 ms per channel for an epoch of EMG of duration 150 ms. A new real time image processing algorithm is proposed to investigate muscle anatomy and activity. Potential applications are proposed in prosthesis control, automatic detection of optimal channels for EMG index extraction and biofeedback. Copyright © 2014 Elsevier Ltd. All rights reserved.

Influence of different attentional focus on EMG amplitude and contraction duration during the bench press at different speeds.

Science.gov (United States)

Calatayud, Joaquin; Vinstrup, Jonas; Jakobsen, Markus D; Sundstrup, Emil; Colado, Juan Carlos; Andersen, Lars L

2018-05-01

The purpose of this study was to investigate whether using different focus affects electromyographic (EMG) amplitude and contraction duration during bench press performed at explosive and controlled speeds. Eighteen young male individuals were familiarized with the procedure and performed the one-maximum repetition (1RM) test in the first session. In the second session, participants performed the bench press exercise at 50% of the 1RM with 3 different attentional focuses (regular focus on moving the load vs contracting the pectoralis vs contracting the triceps) at 2 speed conditions (controlled vs maximal speed). During the controlled speed condition, focusing on using either the pectoralis or the triceps muscles increased pectoralis normalized EMG (nEMG) by 6% (95% CI 3-8%; p = 0.0001) and 4% nEMG (95% CI 1-7%; p = 0.0096), respectively, compared with the regular focus condition. Triceps activity was increased by 4% nEMG (95% CI 0-7%; p = 0.0308) at the controlled speed condition during the triceps focus. During the explosive speed condition, the use of different focuses had no effect. The different attentional focus resulted in comparable contraction duration for the measured muscles when the exercise was performed explosively. Using internal focus to increase EMG amplitude seems to function only during conditions of controlled speed.

Ultra low-power biomedical signal processing: An analog wavelet filter approach for pacemakers

OpenAIRE

Pavlík Haddad, S.A.

2006-01-01

The purpose of this thesis is to describe novel signal processing methodologies and analog integrated circuit techniques for low-power biomedical systems. Physiological signals, such as the electrocardiogram (ECG), the electroencephalogram (EEG) and the electromyogram (EMG) are mostly non-stationary. The main difficulty in dealing with biomedical signal processing is that the information of interest is often a combination of features that are well localized temporally (e.g., spikes) and other...

Effects of Acupuncture Therapy on the EMG Activity of the Rectus Femoris and Tibialis Anterior during Maximal Voluntary Isometric Contraction in College Students

Directory of Open Access Journals (Sweden)

Se In Jang

2017-12-01

Full Text Available Acupuncture has been increasingly used in the treatment of muscle damage associated with sports activities. However, studies on the immediate effects of one-time acupuncture on the muscles of athletes are clearly lacking. Thus, this study aimed to examine the effects of acupuncture therapy on the maximal voluntary isometric contraction (MVIC electromyography (EMG of the rectus femoris and tibialis anterior muscles. This study was conducted among 20 healthy male college students who had no musculoskeletal disease. The participants were subjected to 3 different experimental conditions and subsequently grouped based on these conditions: real acupuncture, sham acupuncture, and control. A 7-day washout period was implemented to avoid any transient effects on the physiological and psychological conditions of the participants. Subsequently, an electromyogram patch was attached on the most developed area in the middle of the origin and insertion of the rectus femoris and tibialis anterior muscles. The percent MVIC, which was used to standardize the signal from the electromyogram, was determined, and the maximal value from the MVIC of the rectus femoris and tibialis anterior muscles was measured. The MVIC EMG activities of both femoris (F = 6.633, p = 0.003 and tibialis anterior (F = 5.216, p = 0.008 muscles were significantly different among all groups. Accordingly, the results of a posthoc test showed that the real acupuncture group had higher MVIC EMG activities in the femoris (p = 0.002 and tibialis anterior (p = 0.006 muscles compared with the control group. These results suggest that treatment with real acupuncture resulted in significantly higher MVIC EMG activities of the rectus femoris and tibialis anterior muscles than the other treatments. Hence, acupuncture may be helpful in the improvement of muscle strength among athletes in the physical fitness field.

EMG activities and plantar pressures during ski jumping take-off on three different sized hills.

Science.gov (United States)

Virmavirta, M; Perttunen, J; Komi, P V

2001-04-01

Different profiles of ski jumping hills have been assumed to make the initiation of take-off difficult especially when moving from one hill to another. Neuromuscular adaptation of ski jumpers to the different jumping hills was examined by measuring muscle activation and plantar pressure of the primary take-off muscles on three different sized hills. Two young ski jumpers volunteered as subjects and they performed several trials from each hill (K-35 m, K-65 m and K-90 m) with the same electromyographic (EMG) electrode and insole pressure transducer set-up. The results showed that the differences in plantar pressure and EMGs between the jumping hills were smaller than expected for both jumpers. The small changes in EMG amplitudes between the hills support the assumption that the take-off was performed with the same intensity on different jumping hills and the timing of the gluteus EMG demonstrates well the similarity of the muscle activation on different hills. On the basis of the results obtained it seems that ski jumping training on small hills does not disturb the movement patterns for bigger hills and can also be helpful for special take-off training with low speed.

Grid investments in a Nordic perspective. Report to EMG

Energy Technology Data Exchange (ETDEWEB)

2010-05-15

In a letter of 20 November 2008, the Electricity Market Group (EMG) under the Nordic Council of Ministers requested NordREG to carry out an assignment related to transmission network investments in the Nordic countries. The assignment to NordREG was divided into two tasks; to map the differences in the legislation and licensing processes in the Nordic countries and to analyse these differences and possible ways of financing common network investment projects. In the second half of 2009 the consultant Econ Poeyry was engaged to support in the finalisation of this project, mainly concerning possibilities for Nordic financing. The final text is however the sole responsibility of the task force. A draft version of the final report was delivered to EMG in December 2009. At the same time the report was sent to the Nordic TSOs together with an invitation to a workshop at Gardermoen on 26 January 2010. The comments from the TSOs are included in appendix 2 of the report

To What Extent Is Mean EMG Frequency during Gait a Reflection of Functional Muscle Strength in Children with Cerebral Palsy?

Science.gov (United States)

Van Gestel, L.; Wambacq, H.; Aertbelien, E.; Meyns, P.; Bruyninckx, H.; Bar-On, L.; Molenaers, G.; De Cock, P.; Desloovere, K.

2012-01-01

The aim of the current paper was to analyze the potential of the mean EMG frequency, recorded during 3D gait analysis (3DGA), for the evaluation of functional muscle strength in children with cerebral palsy (CP). As walking velocity is known to also influence EMG frequency, it was investigated to which extent the mean EMG frequency is a reflection…

Curved Microneedle Array-Based sEMG Electrode for Robust Long-Term Measurements and High Selectivity

Directory of Open Access Journals (Sweden)

Minjae Kim

2015-07-01

Full Text Available Surface electromyography is widely used in many fields to infer human intention. However, conventional electrodes are not appropriate for long-term measurements and are easily influenced by the environment, so the range of applications of sEMG is limited. In this paper, we propose a flexible band-integrated, curved microneedle array electrode for robust long-term measurements, high selectivity, and easy applicability. Signal quality, in terms of long-term usability and sensitivity to perspiration, was investigated. Its motion-discriminating performance was also evaluated. The results show that the proposed electrode is robust to perspiration and can maintain a high-quality measuring ability for over 8 h. The proposed electrode also has high selectivity for motion compared with a commercial wet electrode and dry electrode.

Classification of EEG signals to identify variations in attention during motor task execution.

Science.gov (United States)

Aliakbaryhosseinabadi, Susan; Kamavuako, Ernest Nlandu; Jiang, Ning; Farina, Dario; Mrachacz-Kersting, Natalie

2017-06-01

Brain-computer interface (BCI) systems in neuro-rehabilitation use brain signals to control external devices. User status such as attention affects BCI performance; thus detecting the user's attention drift due to internal or external factors is essential for high detection accuracy. An auditory oddball task was applied to divert the users' attention during a simple ankle dorsiflexion movement. Electroencephalogram signals were recorded from eighteen channels. Temporal and time-frequency features were projected to a lower dimension space and used to analyze the effect of two attention levels on motor tasks in each participant. Then, a global feature distribution was constructed with the projected time-frequency features of all participants from all channels and applied for attention classification during motor movement execution. Time-frequency features led to significantly better classification results with respect to the temporal features, particularly for electrodes located over the motor cortex. Motor cortex channels had a higher accuracy in comparison to other channels in the global discrimination of attention level. Previous methods have used the attention to a task to drive external devices, such as the P300 speller. However, here we focus for the first time on the effect of attention drift while performing a motor task. It is possible to explore user's attention variation when performing motor tasks in synchronous BCI systems with time-frequency features. This is the first step towards an adaptive real-time BCI with an integrated function to reveal attention shifts from the motor task. Copyright © 2017 Elsevier B.V. All rights reserved.

Use of the discriminant Fourier-derived cepstrum with feature-level post-processing for surface electromyographic signal classification

International Nuclear Information System (INIS)

Chen, Xinpu; Zhu, Xiangyang; Zhang, Dingguo

2009-01-01

Myoelectrical pattern classification is a crucial part in multi-functional prosthesis control. This paper investigates a discriminant Fourier-derived cepstrum (DFC) and feature-level post-processing (FLPP) to discriminate hand and wrist motions using the surface electromyographic signal. The Fourier-derived cepstrum takes advantage of the Fourier magnitude or sub-band power energy of signals directly and provides flexible use of spectral information changing with different motions. Appropriate cepstral coefficients are selected by a proposed separability criterion to construct DFC features. For the post-processing, FLPP which combines features from several analysis windows is used to improve the feature performance further. In this work, two classifiers (a linear discriminant classifier and quadratic discriminant classifier) without hyper-parameter optimization are employed to simplify the training procedure and avoid the possible bias of feature evaluation. Experimental results of the 11-motion problem show that the proposed DFC feature outperforms traditional features such as time-domain statistics and autoregressive-derived cepstrum in terms of the classification accuracy, and it is a promising method for the multi-functionality and high-accuracy control of myoelectric prostheses

Engagement and EMG in serious gaming : Experimenting with sound and dynamics in the levee patroller training game

NARCIS (Netherlands)

Schuurink, E.L.; Houtkamp, J.; Toet, A.

2008-01-01

We measured the effects of sound and visual dynamic elements on user experience of a serious game, with special interest in engagement and arousal. Engagement was measured through questionnaires and arousal through the SAM and electromyography (EMG). We adopted the EMG of the corrugator (frown

Analysis of scapular muscle EMG activity in patients with idiopathic neck pain: a systematic review.

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Castelein, Birgit; Cools, Ann; Bostyn, Emma; Delemarre, Jolien; Lemahieu, Trees; Cagnie, Barbara

2015-04-01

It is proposed that altered scapular muscle function can contribute to abnormal loading of the cervical spine. However, it is not clear if patients with idiopathic neck pain show altered activity of the scapular muscles. The aim of this paper was to systematically review the literature regarding the differences or similarities in scapular muscle activity, measured by electromyography ( = EMG), between patients with chronic idiopathic neck pain compared to pain-free controls. Case-control (neck pain/healthy) studies investigating scapular muscle EMG activity (amplitude, timing and fatigue parameters) were searched in Pubmed and Web of Science. 25 articles were included in the systematic review. During rest and activities below shoulder height, no clear differences in mean Upper Trapezius ( = UT) EMG activity exist between patients with idiopathic neck pain and a healthy control group. During overhead activities, no conclusion for scapular EMG amplitude can be drawn as a large variation of results were reported. Adaptation strategies during overhead tasks are not the same between studies. Only one study investigated timing of the scapular muscles and found a delayed onset and shorter duration of the SA during elevation in patients with idiopathic neck pain. For scapular muscle fatigue, no definite conclusions can be made as a wide variation and conflicting results are reported. Further high quality EMG research on scapular muscles (broader than the UT) is necessary to understand/draw conclusions on how scapular muscles react in the presence of idiopathic neck pain. Copyright © 2015 Elsevier Ltd. All rights reserved.

A COMPARATIVE STUDY OF EFFICACY OF EMG BIO-FEEDBACK AND PROGRESSIVE MUSCULAR RELAXATION IN TENSION HEADACHE1

Science.gov (United States)

Gada, M.T.

1984-01-01

SUMMARY The aim of the present study was to find out efficacy of frontalis EMG Biofeedback therapy, deep muscular relaxation therapy and compare the efficacy of both in cases of tension headache. During two week basal-data recording period all patients were taught deep muscular relaxation by Jacobson′s technique. Simultaneously patients were instructed to keep headache diary. Headache diary yielded three different parameters a) number of headache-free days per week, b) peak headache intensity (or each week and c) average daily headache activity score per week. These parameters were used to find out therapeutic efficacy of each treatment. Patients were randomly divided in two groups. EMG Biofeedback group was given frontalis EMG feedback through EMG J 33 muscle trainer of Cyborg Corporation (U.S.A.). Patients in each group were given 20 sessions (two sessions per week); each session lasting 30 minutes. Patients were instructed to practice at least one 30 minute session of relaxation at home. The data were subjected to statistical calculation. The results indicate that frontalis EMG Biofeedback therapy and deep muscle relaxation therapy are significantly effective in cases of tension headache. Both treatments are equally effective. The findings are discussed in relation to Indian situation. PMID:21965970

Fabrication of a Micro-Needle Array Electrode by Thermal Drawing for Bio-Signals Monitoring.

Science.gov (United States)

Ren, Lei; Jiang, Qing; Chen, Keyun; Chen, Zhipeng; Pan, Chengfeng; Jiang, Lelun

2016-06-17

A novel micro-needle array electrode (MAE) fabricated by thermal drawing and coated with Ti/Au film was proposed for bio-signals monitoring. A simple and effective setup was employed to form glassy-state poly (lactic-co-glycolic acid) (PLGA) into a micro-needle array (MA) by the thermal drawing method. The MA was composed of 6 × 6 micro-needles with an average height of about 500 μm. Electrode-skin interface impedance (EII) was recorded as the insertion force was applied on the MAE. The insertion process of the MAE was also simulated by the finite element method. Results showed that MAE could insert into skin with a relatively low compression force and maintain stable contact impedance between the MAE and skin. Bio-signals, including electromyography (EMG), electrocardiography (ECG), and electroencephalograph (EEG) were also collected. Test results showed that the MAE could record EMG, ECG, and EEG signals with good fidelity in shape and amplitude in comparison with the commercial Ag/AgCl electrodes, which proves that MAE is an alternative electrode for bio-signals monitoring.

Fabrication of a Micro-Needle Array Electrode by Thermal Drawing for Bio-Signals Monitoring

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Lei Ren

2016-06-01

Full Text Available A novel micro-needle array electrode (MAE fabricated by thermal drawing and coated with Ti/Au film was proposed for bio-signals monitoring. A simple and effective setup was employed to form glassy-state poly (lactic-co-glycolic acid (PLGA into a micro-needle array (MA by the thermal drawing method. The MA was composed of 6 × 6 micro-needles with an average height of about 500 μm. Electrode-skin interface impedance (EII was recorded as the insertion force was applied on the MAE. The insertion process of the MAE was also simulated by the finite element method. Results showed that MAE could insert into skin with a relatively low compression force and maintain stable contact impedance between the MAE and skin. Bio-signals, including electromyography (EMG, electrocardiography (ECG, and electroencephalograph (EEG were also collected. Test results showed that the MAE could record EMG, ECG, and EEG signals with good fidelity in shape and amplitude in comparison with the commercial Ag/AgCl electrodes, which proves that MAE is an alternative electrode for bio-signals monitoring.

Automated real-time detection of tonic-clonic seizures using a wearable EMG device

DEFF Research Database (Denmark)

Beniczky, Sándor; Conradsen, Isa; Henning, Oliver

2018-01-01

OBJECTIVE: To determine the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) using a wearable surface EMG device. METHODS: We prospectively tested the technical performance and diagnostic accuracy of real-time seizure detection using a wearable surface EMG device....... The seizure detection algorithm and the cutoff values were prespecified. A total of 71 patients, referred to long-term video-EEG monitoring, on suspicion of GTCS, were recruited in 3 centers. Seizure detection was real-time and fully automated. The reference standard was the evaluation of video-EEG recordings...

Comparison of Electrocardiogram Signals in Men and Women during Creativity with Classification Approaches

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Sahar ZAKERI

2016-07-01

Full Text Available Electrocardiogram (ECG analysis is mostly used as a valuable tool in the evaluation of cognitive tasks. By taking and analyzing measurements in vast quantities, researchers are working toward a better understanding of how human physiological systems work. For the first time, this study investigated the function of the cardiovascular system during creative thinking. In addition, the difference between male/female and normal/creativity states from ECG signals was investigated. Overall, the purpose of this paper was to illustrate the heart working during the creativity, and discover the creative men or women subjects. For these goals, six nonlinear features of the ECG signal were extracted to detect creativity states. During the three tasks of the Torrance Tests of Creative Thinking (TTCT- Figural B, ECG signals were recorded from 52 participants (26 men and 26 women. Then, the proficiency of two kinds of classification approaches was evaluated: Artificial Neural Network (ANN and Support Vector Machine (SVM. The results indicated the high accuracy rate of discriminations between male/female (96.09% and normal/creativity states (95.84% using ANN classifier. Therefore, the proposed method can be useful to detect the creativity states.

Mandibular kinematics and masticatory muscles EMG in patients with short lasting TMD of mild-moderate severity.

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De Felício, Cláudia Maria; Mapelli, Andrea; Sidequersky, Fernanda Vincia; Tartaglia, Gianluca M; Sforza, Chiarella

2013-06-01

Mandibular kinematic and standardized surface electromyography (sEMG) characteristics of masticatory muscles of subjects with short lasting TMD of mild-moderate severity were examined. Volunteers were submitted to clinical examination and questionnaire of severity. Ten subjects with TMD (age 27.3years, SD 7.8) and 10 control subjects without TMD, matched by age, were selected. Mandibular movements were recorded during free maximum mouth opening and closing (O-C) and unilateral, left and right, gum chewing. sEMG of the masseter and temporal muscles was performed during maximum teeth clenching either on cotton rolls or in intercuspal position, and during gum chewing. sEMG indices were obtained. Subjects with TMD, relative to control subjects, had lower relative mandibular rotation at the end of mouth opening, larger mean number of intersection between interincisal O-C paths during mastication and smaller asymmetry between working and balancing side, with participation beyond the expected of the contralateral muscles (Pkinematic parameters and the EMG indices of the static test, although some changes in the mastication were observed. Copyright © 2013 Elsevier Ltd. All rights reserved.

Affective Man-Machine Interface: Unveiling Human Emotions through Biosignals

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van den Broek, Egon L.; Lisý, Viliam; Janssen, Joris H.; Westerink, Joyce H. D. M.; Schut, Marleen H.; Tuinenbreijer, Kees

As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological proce-sses, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals.

Classification of Parameters Extracted from Cardiotocographic Signals for Early Detection of Metabolic Acidemia in Newborns

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ROTARIU, C.

2015-08-01

Full Text Available Fetal acidosis is reflected by the values of umbilical cord pH and base deficit (BDecf: normal recordings (pH over 7.2 and BDecf under 8 mmol/l and abnormal recordings (pH under 7.2 and BDecf over 8 mmol/l. The purpose of this paper is to present the implementation of an automated system for detecting fetal acidosis in cardiotocographic recordings. The method uses spectral analysis of medium (0.07-0.13 Hz and high (0.13-1 Hz frequency spectrum. We implement the algorithm for segments of the recordings without signal loss for better classification. We determined the normalized medium and high frequency components and mid to high frequency ratio. The recordings in the database are divided into a control group (100 normal recordings and a test group (431 normal or abnormal recordings. A t-test with the p value under 0.05 between the two groups is used to classify the test group. The classification is improved by including the presence of late and prolonged decelerations in the classification process, obtaining the final results, which are comparable to the best ones in current literature.

Comparison of Standard and Novel Signal Analysis Approaches to Obstructive Sleep Apnoea Classification

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Aoife eRoebuck

2015-08-01

Full Text Available Obstructive sleep apnoea (OSA is a disorder characterised by repeated pauses in breathing during sleep, which leads to deoxygenation and voiced chokes at the end of each episode. OSA is associated by daytime sleepiness and an increased risk of serious conditions such as cardiovascular disease, diabetes and stroke. Between 2-7% of the adult population globally has OSA, but it is estimated that up to 90% of those are undiagnosed and untreated. Diagnosis of OSA requires expensive and cumbersome screening. Audio offers a potential non-contact alternative, particularly with the ubiquity of excellent signal processing on every phone.Previous studies have focused on the classification of snoring and apnoeic chokes. However, such approaches require accurate identification of events. This leads to limited accuracy and small study populations. In this work we propose an alternative approach which uses multiscale entropy (MSE coefficients presented to a classifier to identify disorder in vocal patterns indicative of sleep apnoea. A database of 858 patients was used, the largest reported in this domain. Apnoeic choke, snore, and noise events encoded with speech analysis features were input into a linear classifier. Coefficients of MSE derived from the first 4 hours of each recording were used to train and test a random forest to classify patients as apnoeic or not.Standard speech analysis approaches for event classification achieved an out of sample accuracy (Ac of 76.9% with a sensitivity (Se of 29.2% and a specificity (Sp of 88.7% but high variance. For OSA severity classification, MSE provided an out of sample Ac of 79.9%, Se of 66.0% and Sp = 88.8%. Including demographic information improved the MSE-based classification performance to Ac = 80.5%, Se = 69.2%, Sp = 87.9%. These results indicate that audio recordings could be used in screening for OSA, but are generally under-sensitive.

Changes in force, surface and motor unit EMG during post-exercise development of low frequency fatigue in vastus lateralis muscle.

Science.gov (United States)

de Ruiter, C J; Elzinga, M J H; Verdijk, P W L; van Mechelen, W; de Haan, A

2005-08-01

We investigated the effects of low frequency fatigue (LFF) on post-exercise changes in rectified surface EMG (rsEMG) and single motor unit EMG (smuEMG) in vastus lateralis muscle (n = 9). On two experimental days the knee extensors were fatigued with a 60-s-isometric contraction (exercise) at 50% maximal force capacity (MFC). On the first day post-exercise (15 s, 3, 9, 15, 21 and 27 min) rsEMG and electrically-induced (surface stimulation) forces were investigated. SmuEMG was obtained on day two. During short ramp and hold (5 s) contractions at 50% MFC, motor unit discharges of the same units were followed over time. Post-exercise MFC and tetanic force (100 Hz stimulation) recovered to about 90% of the pre-exercise values, but recovery with 20 Hz stimulation was less complete: the 20-100 Hz force ratio (mean +/- SD) decreased from 0.65+/-0.06 (pre-exercise) to 0.56+/-0.04 at 27 min post-exercise (Pexercise rsEMG (% pre-exercise maximum) and motor unit discharge rate were 51.1 +/- 12.7% and 14.1 +/- 3.7 (pulses per second; pps) respectively, 15 s post-exercise the respective values were 61.4 +/- 15.4% (P0.05). Thereafter, rsEMG (at 50% MFC) remained stable but motor unit discharge rate significantly increased to 17.7 +/- 3.9 pps 27 min post-exercise. The recruitment threshold decreased (Pexercise to 25.2 +/- 6.7% 27 min post-exercise. The increase in discharge rate was significantly greater than could be expected from the decrease in recruitment threshold. Thus, post-exercise LFF was compensated by increased motor unit discharge rates which could only partly be accounted for by the small decrease in motor unit recruitment threshold.

EMG System for Production of Methane From Carbon Dioxide, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — Sustainable Innovations, LLC, is developing an Electrochemical Methane Generator (EMG), which comprises a novel method of converting CO2 and H2O to hydrocarbon fuels...

EMG changes in thigh and calf muscles in fin swimming exercise.

Science.gov (United States)

Jammes, Y; Delliaux, S; Coulange, M; Jammes, C; Kipson, N; Brerro-Saby, C; Bregeon, F

2010-08-01

Because previous researchers have reported a reduced lactic acid production that accompanies a delayed or an absent ventilatory threshold (VTh) in water-based exercise, we hypothesized that the metaboreflex, activated by muscle acidosis, might be absent in fin swimming. This motor response, delaying the occurrence of fatigue, is characterized by a decreased median frequency (MF) of electromyographic (EMG) power spectrum. Seven healthy subjects performed a maximal fin swimming exercise protocol with simultaneous recordings of surface EMGs in VASTUS MEDIALIS (VM), TIBIALIS ANTERIOR (TA) and GASTROCNEMIUS MEDIALIS (GM). We computed the root mean square (RMS) and MF and recorded the compound evoked muscle potential (M-wave) in VM. We also measured the propulsive force and oxygen uptake (VO (2)), and determined VTh. VTh was absent in 4/7 subjects and measured at 70-90% of VO (2max) in the other three. In the three studied muscles, the global EMG activity (RMS) increased while the MF decreased in proportion of VO (2), the MF changes being significantly higher in VM (-29%) and GM (-39%) than in TA (-19%). Because no M-wave changes were noted, the MF decline was attributed to the recruitment of low-frequency, fatigue-resistant motor units. Our most important finding is the persistence of the metaboreflex even in a situation of reduced muscle acidosis. (c) Georg Thieme Verlag KG Stuttgart . New York.

Adaptive Admittance Control for an Ankle Exoskeleton Using an EMG-Driven Musculoskeletal Model

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Shaowei Yao

2018-04-01

Full Text Available Various rehabilitation robots have been employed to recover the motor function of stroke patients. To improve the effect of rehabilitation, robots should promote patient participation and provide compliant assistance. This paper proposes an adaptive admittance control scheme (AACS consisting of an admittance filter, inner position controller, and electromyography (EMG-driven musculoskeletal model (EDMM. The admittance filter generates the subject's intended motion according to the joint torque estimated by the EDMM. The inner position controller tracks the intended motion, and its parameters are adjusted according to the estimated joint stiffness. Eight healthy subjects were instructed to wear the ankle exoskeleton robot, and they completed a series of sinusoidal tracking tasks involving ankle dorsiflexion and plantarflexion. The robot was controlled by the AACS and a non-adaptive admittance control scheme (NAACS at four fixed parameter levels. The tracking performance was evaluated using the jerk value, position error, interaction torque, and EMG levels of the tibialis anterior (TA and gastrocnemius (GAS. For the NAACS, the jerk value and position error increased with the parameter levels, and the interaction torque and EMG levels of the TA tended to decrease. In contrast, the AACS could maintain a moderate jerk value, position error, interaction torque, and TA EMG level. These results demonstrate that the AACS achieves a good tradeoff between accurate tracking and compliant assistance because it can produce a real-time response to stiffness changes in the ankle joint. The AACS can alleviate the conflict between accurate tracking and compliant assistance and has potential for application in robot-assisted rehabilitation.

Independence Between Two Channels of Surface Electromyogram Signal to Measure the Loss of Motor Units

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Arjunan Sridhar P.

2015-06-01

Full Text Available This study has investigated the relationship in the connectivity of motor units in surface electromyogram (sEMG of biceps brachii muscle. It is hypothesized that with ageing, there is reduction/loss in number of motor units, leading to reduction in the independence between the channels of the recorded muscle activity. Two channels of sEMG were recorded during three levels of isometric muscle contraction: 50 %, 75 % and 100 % maximal voluntary contraction (MVC. 73 subjects (age range 20-70 participated in the experiments. The independence in channel index (ICI between the two sEMG recording locations was computed using the independent components and Frobenius norm. ANOVA Statistical analysis was performed to test the effect of age (loss of motor units and level of contraction on ICI. The results show that the ICI among the older cohort was significantly lower compared with the younger adults. This research study has shown that the reduction in number of motor units is reflected by the reduction in the ICI of the sEMG signal.

Analysis of progression of fatigue conditions in biceps brachii muscles using surface electromyography signals and complexity based features.

Science.gov (United States)

Karthick, P A; Makaram, Navaneethakrishna; Ramakrishnan, S

2014-01-01

Muscle fatigue is a neuromuscular condition where muscle performance decreases due to sustained or intense contraction. It is experienced by both normal and abnormal subjects. In this work, an attempt has been made to analyze the progression of muscle fatigue in biceps brachii muscles using surface electromyography (sEMG) signals. The sEMG signals are recorded from fifty healthy volunteers during dynamic contractions under well defined protocol. The acquired signals are preprocessed and segmented in to six equal parts for further analysis. The features, such as activity, mobility, complexity, sample entropy and spectral entropy are extracted from all six zones. The results are found showing that the extracted features except complexity feature have significant variations in differentiating non-fatigue and fatigue zone respectively. Thus, it appears that, these features are useful in automated analysis of various neuromuscular activities in normal and pathological conditions.

Evaluation of Linear Regression Simultaneous Myoelectric Control Using Intramuscular EMG.

Science.gov (United States)

Smith, Lauren H; Kuiken, Todd A; Hargrove, Levi J

2016-04-01

The objective of this study was to evaluate the ability of linear regression models to decode patterns of muscle coactivation from intramuscular electromyogram (EMG) and provide simultaneous myoelectric control of a virtual 3-DOF wrist/hand system. Performance was compared to the simultaneous control of conventional myoelectric prosthesis methods using intramuscular EMG (parallel dual-site control)-an approach that requires users to independently modulate individual muscles in the residual limb, which can be challenging for amputees. Linear regression control was evaluated in eight able-bodied subjects during a virtual Fitts' law task and was compared to performance of eight subjects using parallel dual-site control. An offline analysis also evaluated how different types of training data affected prediction accuracy of linear regression control. The two control systems demonstrated similar overall performance; however, the linear regression method demonstrated improved performance for targets requiring use of all three DOFs, whereas parallel dual-site control demonstrated improved performance for targets that required use of only one DOF. Subjects using linear regression control could more easily activate multiple DOFs simultaneously, but often experienced unintended movements when trying to isolate individual DOFs. Offline analyses also suggested that the method used to train linear regression systems may influence controllability. Linear regression myoelectric control using intramuscular EMG provided an alternative to parallel dual-site control for 3-DOF simultaneous control at the wrist and hand. The two methods demonstrated different strengths in controllability, highlighting the tradeoff between providing simultaneous control and the ability to isolate individual DOFs when desired.

A Review of Sleep Disorder Diagnosis by Electromyogram Signal Analysis.

Science.gov (United States)

Shokrollahi, Mehrnaz; Krishnan, Sridhar

2015-01-01

Sleep and sleep-related problems play a role in a large number of human disorders and affect every field of medicine. It is estimated that 50 to 70 million Americans suffer from a chronic sleep disorder, which hinders their daily life, affects their health, and confers a significant economic burden to society. The negative public health consequences of sleep disorders are enormous and could have long-term effects, including increased risk of hypertension, diabetes, obesity, heart attack, stroke and in some cases death. Polysomnographic modalities can monitor sleep cycles to identify disrupted sleep patterns, adjust the treatments, increase therapeutic options and enhance the quality of life of recording the electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG). Although the skills acquired by medical facilitators are quite extensive, it is just as important for them to have access to an assortment of technologies and to further improve their monitoring and treatment capabilities. Computer-aided analysis is one advantageous technique that could provide quantitative indices for sleep disorder screening. Evolving evidence suggests that Parkinson's disease may be associated with rapid eye movement sleep behavior disorder (RBD). With this article, we are reviewing studies that are related to EMG signal analysis for detection of neuromuscular diseases that result from sleep movement disorders. As well, the article describes the recent progress in analysis of EMG signals using temporal analysis, frequency-domain analysis, time-frequency, and sparse representations, followed by the comparison of the recent research.

Comparison of jaw muscle EMG activity in awake and sleep bruxers

DEFF Research Database (Denmark)

Castrillon, Eduardo; Dreyer Nielsen, Patricia; Haugland, Morten

2015-01-01

, Scandinavian Center for Orofacial Neuroscience (SCON), Aarhus, DENMARK; M. Haugland, DELTA, Copenhagen, DENMARK; W. Yachida, T. Arima, Hokkaido University, Hokkaido, JAPAN; Group Author Abstracts: ABSTRACT: Objectives: Background: Bruxism has two different circadian manifestations (awake and sleep) that have...... been proposed to have different underlying pathophysiology. Objectives: To compare the characteristics of multiple days EMG assessment of the anterior temporalis muscles between patients with self-reported awake bruxism, sleep bruxism and healthy individuals. Methods: Methods: Participants...... with possible sleep bruxism (n=9) or awake bruxism (n=9) bruxism were included in an open study and compared with healthy individuals during awake (n=9) and sleep (n=7) states. All participants were assessed for a minimum of 4 days (awake or sleep) with a portable single-channel EMG recorder. The outcome...

Fractal and twin SVM-based handgrip recognition for healthy subjects and trans-radial amputees using myoelectric signal.

Science.gov (United States)

Arjunan, Sridhar Poosapadi; Kumar, Dinesh Kant; Jayadeva J

2016-02-01

Identifying functional handgrip patterns using surface electromygram (sEMG) signal recorded from amputee residual muscle is required for controlling the myoelectric prosthetic hand. In this study, we have computed the signal fractal dimension (FD) and maximum fractal length (MFL) during different grip patterns performed by healthy and transradial amputee subjects. The FD and MFL of the sEMG, referred to as the fractal features, were classified using twin support vector machines (TSVM) to recognize the handgrips. TSVM requires fewer support vectors, is suitable for data sets with unbalanced distributions, and can simultaneously be trained for improving both sensitivity and specificity. When compared with other methods, this technique resulted in improved grip recognition accuracy, sensitivity, and specificity, and this improvement was significant (κ=0.91).

Effectiveness of Multivariate Time Series Classification Using Shapelets

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A. P. Karpenko

2015-01-01

Full Text Available Typically, time series classifiers require signal pre-processing (filtering signals from noise and artifact removal, etc., enhancement of signal features (amplitude, frequency, spectrum, etc., classification of signal features in space using the classical techniques and classification algorithms of multivariate data. We consider a method of classifying time series, which does not require enhancement of the signal features. The method uses the shapelets of time series (time series shapelets i.e. small fragments of this series, which reflect properties of one of its classes most of all.Despite the significant number of publications on the theory and shapelet applications for classification of time series, the task to evaluate the effectiveness of this technique remains relevant. An objective of this publication is to study the effectiveness of a number of modifications of the original shapelet method as applied to the multivariate series classification that is a littlestudied problem. The paper presents the problem statement of multivariate time series classification using the shapelets and describes the shapelet–based basic method of binary classification, as well as various generalizations and proposed modification of the method. It also offers the software that implements a modified method and results of computational experiments confirming the effectiveness of the algorithmic and software solutions.The paper shows that the modified method and the software to use it allow us to reach the classification accuracy of about 85%, at best. The shapelet search time increases in proportion to input data dimension.

EMG Biofeedback Training Versus Systematic Desensitization for Test Anxiety Reduction

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Romano, John L.; Cabianca, William A.

1978-01-01

Biofeedback training to reduce test anxiety among university students was investigated. Biofeedback training with systematic desensitization was compared to an automated systematic desensitization program not using EMG feedback. Biofeedback training is a useful technique for reducing test anxiety, but not necessarily more effective than systematic…

A portable system for acquiring and removing motion artefact from ECG signals

Science.gov (United States)

Griffiths, A.; Das, A.; Fernandes, B.; Gaydecki, P.

2007-07-01

A novel electrocardiograph (ECG) signal acquisition and display system is under development. It is designed for patients ranging from the elderly to athletes. The signals are obtained from electrodes integrated into a vest, amplified, digitally processed and transmitted via Bluetooth to a PC with a Labview ® interface. Digital signal processing is performed to remove movement artefact and electromyographic (EMG) noise, which severely distorts signal morphology and complicates clinical diagnosis. Independent component analysis (ICA) is also used to improve the signal quality. The complete system will integrate the electronics into a single module which will be embedded in the vest.

A portable system for acquiring and removing motion artefact from ECG signals

Energy Technology Data Exchange (ETDEWEB)

Griffiths, A; Das, A; Fernandes, B; Gaydecki, P [School of Electrical and Electronic Engineering, University of Manchester, PO Box 88, Manchester M60 1QD (United Kingdom)

2007-07-15

A novel electrocardiograph (ECG) signal acquisition and display system is under development. It is designed for patients ranging from the elderly to athletes. The signals are obtained from electrodes integrated into a vest, amplified, digitally processed and transmitted via Bluetooth to a PC with a Labview (registered) interface. Digital signal processing is performed to remove movement artefact and electromyographic (EMG) noise, which severely distorts signal morphology and complicates clinical diagnosis. Independent component analysis (ICA) is also used to improve the signal quality. The complete system will integrate the electronics into a single module which will be embedded in the ves000.

A novel, fast and efficient single-sensor automatic sleep-stage classification based on complementary cross-frequency coupling estimates.

Science.gov (United States)

Dimitriadis, Stavros I; Salis, Christos; Linden, David

2018-04-01

Limitations of the manual scoring of polysomnograms, which include data from electroencephalogram (EEG), electro-oculogram (EOG), electrocardiogram (ECG) and electromyogram (EMG) channels have long been recognized. Manual staging is resource intensive and time consuming, and thus considerable effort must be spent to ensure inter-rater reliability. As a result, there is a great interest in techniques based on signal processing and machine learning for a completely Automatic Sleep Stage Classification (ASSC). In this paper, we present a single-EEG-sensor ASSC technique based on the dynamic reconfiguration of different aspects of cross-frequency coupling (CFC) estimated between predefined frequency pairs over 5 s epoch lengths. The proposed analytic scheme is demonstrated using the PhysioNet Sleep European Data Format (EDF) Database with repeat recordings from 20 healthy young adults. We validate our methodology in a second sleep dataset. We achieved very high classification sensitivity, specificity and accuracy of 96.2 ± 2.2%, 94.2 ± 2.3%, and 94.4 ± 2.2% across 20 folds, respectively, and also a high mean F1 score (92%, range 90-94%) when a multi-class Naive Bayes classifier was applied. High classification performance has been achieved also in the second sleep dataset. Our method outperformed the accuracy of previous studies not only on different datasets but also on the same database. Single-sensor ASSC makes the entire methodology appropriate for longitudinal monitoring using wearable EEG in real-world and laboratory-oriented environments. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

Locomotor training with body weight support in SCI: EMG improvement is more optimally expressed at a low testing speed.

Science.gov (United States)

Meyns, P; Van de Crommert, H W A A; Rijken, H; van Kuppevelt, D H J M; Duysens, J

2014-12-01

Case series. To determine the optimal testing speed at which the recovery of the EMG (electromyographic) activity should be assessed during and after body weight supported (BWS) locomotor training. Tertiary hospital, Sint Maartenskliniek, Nijmegen, The Netherlands. Four participants with incomplete chronic SCI were included for BWS locomotor training; one AIS-C and three AIS-D (according to the ASIA (American Spinal Injury Association) Impairment Scale or AIS). All were at least 5 years after injury. The SCI participants were trained three times a week for a period of 6 weeks. They improved their locomotor function in terms of higher walking speed, less BWS and less assistance needed. To investigate which treadmill speed for EMG assessment reflects the functional improvement most adequately, all participants were assessed weekly using the same two speeds (0.5 and 1.5 km h(-1), referred to as low and high speed, respectively) for 6 weeks. The change in root mean square EMG (RMS EMG) was assessed in four leg muscles; biceps femoris, rectus femoris, gastrocnemius medialis and tibialis anterior. The changes in RMS EMG occurred at similar phases of the step cycle for both walking conditions, but these changes were larger when the treadmill was set at a low speed (0.5 km h(-1)). Improvement in gait is feasible with BWS treadmill training even long after injury. The EMG changes after treadmill training are more optimally expressed using a low rather than a high testing treadmill speed.

Specific diurnal EMG activity pattern observed in occlusal collapse patients: relationship between diurnal bruxism and tooth loss progression.

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Kawakami, Shigehisa; Kumazaki, Yohei; Manda, Yosuke; Oki, Kazuhiro; Minagi, Shogo

2014-01-01

The role of parafunctional masticatory muscle activity in tooth loss has not been fully clarified. This study aimed to reveal the characteristic activity of masseter muscles in bite collapse patients while awake and asleep. Six progressive bite collapse patients (PBC group), six age- and gender-matched control subjects (MC group), and six young control subjects (YC group) were enrolled. Electromyograms (EMG) of the masseter muscles were continuously recorded with an ambulatory EMG recorder while patients were awake and asleep. Diurnal and nocturnal parafunctional EMG activity was classified as phasic, tonic, or mixed using an EMG threshold of 20% maximal voluntary clenching. Highly extended diurnal phasic activity was observed only in the PBC group. The three groups had significantly different mean diurnal phasic episodes per hour, with 13.29±7.18 per hour in the PBC group, 0.95±0.97 per hour in the MC group, and 0.87±0.98 per hour in the YC group (pstability.

An EMG-Controlled SMA Device for the Rehabilitation of the Ankle Joint in Post-Acute Stroke

Science.gov (United States)

Pittaccio, S.; Viscuso, S.

2011-07-01

The capacity of flexing one's ankle is an indispensible segment of gait re-learning, as imbalance, wrong compensatory use of other joints and risk of falling may depend on the so-called drop-foot. The rehabilitation of ankle dorsiflexion may be achieved through active exercising of the relevant musculature (especially tibialis anterior, TA). This can be troublesome for patients affected by weakness and flaccid paresis. Thus, as needs evolve during patient's improvements, a therapeutic device should be able to guide and sustain gradual recovery by providing commensurate aid. This includes exploiting even initial attempts at voluntary motion and turns those into effective workout. An active orthosis powered by two rotary actuators containing NiTi wire was designed to obtain ankle dorsiflexion. A computer routine that analyzes the electromyographic (sEMG) signal from TA muscle is used to control the orthosis and trigger its activation. The software also provides instructions and feed-back for the patient. Tests on the orthosis proved that it can produce strokes up to 36° against resisting torques exceeding 180 Ncm. Three healthy subjects were able to control the orthosis by modulating their TA sEMG activity. The movement produced in the preliminary tests is interesting for lower limb rehabilitation, and will be further improved by optimizing body-orthosis interface. It is hoped that this device will enhance early rehabilitation and recovery of ankle mobility in stroke patients.

Application of complex discrete wavelet transform in classification of Doppler signals using complex-valued artificial neural network.

Science.gov (United States)

Ceylan, Murat; Ceylan, Rahime; Ozbay, Yüksel; Kara, Sadik

2008-09-01

In biomedical signal classification, due to the huge amount of data, to compress the biomedical waveform data is vital. This paper presents two different structures formed using feature extraction algorithms to decrease size of feature set in training and test data. The proposed structures, named as wavelet transform-complex-valued artificial neural network (WT-CVANN) and complex wavelet transform-complex-valued artificial neural network (CWT-CVANN), use real and complex discrete wavelet transform for feature extraction. The aim of using wavelet transform is to compress data and to reduce training time of network without decreasing accuracy rate. In this study, the presented structures were applied to the problem of classification in carotid arterial Doppler ultrasound signals. Carotid arterial Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group included 22 males and 16 females with an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal (lower extremity) angiographies (mean age, 59 years; range, 48-72 years). Healthy volunteers were young non-smokers who seem to not bear any risk of atherosclerosis, including 28 males and 12 females (mean age, 23 years; range, 19-27 years). Sensitivity, specificity and average detection rate were calculated for comparison, after training and test phases of all structures finished. These parameters have demonstrated that training times of CVANN and real-valued artificial neural network (RVANN) were reduced using feature extraction algorithms without decreasing accuracy rate in accordance to our aim.

Thinking Outside the Button Box: EMG as a Computer Input Device for Psychological Research

Directory of Open Access Journals (Sweden)

L. Elizabeth Crawford

2017-07-01

Full Text Available Experimental psychology research commonly has participants respond to stimuli by pressing buttons or keys. Standard computer input devices constrain the range of motoric responses participants can make, even as the field advances theory about the importance of the motor system in cognitive and social information processing. Here we describe an inexpensive way to use an electromyographic (EMG signal as a computer input device, enabling participants to control a computer by contracting muscles that are not usually used for that purpose, but which may be theoretically relevant. We tested this approach in a study of facial mimicry, a well-documented phenomenon in which viewing emotional faces elicits automatic activation of corresponding muscles in the face of the viewer. Participants viewed happy and angry faces and were instructed to indicate the emotion on each face as quickly as possible by either furrowing their brow or contracting their cheek. The mapping of motor response to judgment was counterbalanced, so that one block of trials required a congruent mapping (contract brow to respond “angry,” cheek to respond “happy” and the other block required an incongruent mapping (brow for “happy,” cheek for “angry”. EMG sensors placed over the left corrugator supercilii muscle and left zygomaticus major muscle fed readings of muscle activation to a microcontroller, which sent a response to a computer when activation reached a pre-determined threshold. Response times were faster when the motor-response mapping was congruent than when it was incongruent, extending prior studies on facial mimicry. We discuss further applications of the method for research that seeks to expand the range of human-computer interaction beyond the button box.

Spatial distribution of surface EMG on trapezius and lumbar muscles of violin and cello players in single note playing.

Science.gov (United States)

Afsharipour, Babak; Petracca, Francesco; Gasparini, Mauro; Merletti, Roberto

2016-12-01

Musicians activate their muscles in different patterns, depending on their posture, the instrument being played, and their experience level. Bipolar surface electrodes have been used in the past to monitor such activity, but this method is highly sensitive to the location of the electrode pair. In this work, the spatial distribution of surface EMG (sEMG) of the right trapezius and right and left erector spinae muscles were studied in 16 violin players and 11 cello players. Musicians played their instrument one string at a time in sitting position with/without backrest support. A 64 sEMG electrode (16×4) grid, 10mm inter-electrode distance (IED), was placed over the middle and lower trapezius (MT and LT) of the bowing arm. Two 16×2 electrode grids (IED=10mm) were placed on the left and right erector spinae muscles. Subjects played each of the four strings of the instrument either in large (1bow/s) or detaché tip/tail (8bows/s) bowing in two sessions (two days). In each of two days, measurements were repeated after half an hour of exercise to see the effect of exercise on the muscle activity and signal stability. A "muscle activity index" (MAI) was defined as the spatial average of the segmented active region of the RMS map. Spatial maps were automatically segmented using the watershed algorithm and thresholding. Results showed that, for violin players, sliding the bow upward from the tip toward the tail results in a higher MAI for the trapezius muscle than a downward bow. On the contrary, in cello players, higher MAI is produced in the tail to tip movement. For both instruments, an increasing MAI in the trapezius was observed as the string position became increasingly lateral, from string 1 (most medial) toward string 4 (most lateral). Half an hour of performance did not cause significant differences between the signal quality and the MAI values measured before and after the exercise. The MAI of the left and right erector spinae was smaller in the case of

A soft robotic exomusculature glove with integrated sEMG sensing for hand rehabilitation.

Science.gov (United States)

Delph, Michael A; Fischer, Sarah A; Gauthier, Phillip W; Luna, Carlos H Martinez; Clancy, Edward A; Fischer, Gregory S

2013-06-01

Stroke affects 750,000 people annually, and 80% of stroke survivors are left with weakened limbs and hands. Repetitive hand movement is often used as a rehabilitation technique in order to regain hand movement and strength. In order to facilitate this rehabilitation, a robotic glove was designed to aid in the movement and coordination of gripping exercises. This glove utilizes a cable system to open and close a patients hand. The cables are actuated by servomotors, mounted in a backpack weighing 13.2 lbs including battery power sources. The glove can be controlled in terms of finger position and grip force through switch interface, software program, or surface myoelectric (sEMG) signal. The primary control modes of the system provide: active assistance, active resistance and a preprogrammed mode. This project developed a working prototype of the rehabilitative robotic glove which actuates the fingers over a full range of motion across one degree-of-freedom, and is capable of generating a maximum 15N grip force.

The utility of EMG interference pattern analysis in botulinum toxin treatment of torticollis: A randomised, controlled and blinded study

DEFF Research Database (Denmark)

Werdelin, L; Dalager, T; Fuglsang-Frederiksen, Anders

2011-01-01

OBJECTIVE: The significance of electromyography (EMG) guidance in botulinum toxin (BT) treatment has been much debated. The aim of this study was to evaluate if EMG guidance in the treatment of torticollis in BT-naive patients had a better outcome than treatment after clinical evaluation alone...

Relationship between oxygen uptake slow component and surface EMG during heavy exercise in humans: influence of pedal rate.

Science.gov (United States)

Vercruyssen, Fabrice; Missenard, Olivier; Brisswalter, Jeanick

2009-08-01

The aim of this study was to test the hypothesis that extreme pedal rates contributed to the slow component of oxygen uptake (VO(2) SC) in association with changes in surface electromyographic (sEMG) during heavy-cycle exercise. Eight male trained cyclists performed two square-wave transitions at 50 and 110 rpm at a work rate that would elicit a VO(2) corresponding to 50% of the difference between peak VO(2) and the ventilatory threshold. Pulmonary gas exchange was measured breath-by-breath and sEMG was obtained from the vastus lateralis and medialis muscles. Integrated EMG flow (QiEMG) and mean power frequency (MPF) were computed. The relative amplitude of the VO(2) SC was significantly higher during the 110-rpm bout (556+/-186 ml min(-1), Pexercise only during the 110-rpm bout and were associated with the greater amplitude of the VO(2) SC observed for this condition (Pmotor units recruitment pattern, muscle energy turnover and muscle temperature have been suggested to explain the different VO(2) SC to heavy pedal rate bouts.