EMG signal processing

Authentication is most important for security. There are many different systems for recognizing the person. The traditional authentication systems such as passwords have drawbacks. It is easy to be stolen. Biometric authentication systems provide the best security. However, a current technique that widely used for identification which is fingerprint has its own disadvantages. Furthermore, current techniques such as facial recognition, iris recognition and voice recognition that used to recognize person still compromise the security walls. In this recent years, electroencephalograph (EEG) signal has been discovered that it has the potential to become one of the biometric authentication systems. It is brain activities for a human. It is unique due to the EEG signal is different from person to person. In this paper, power spectral density analysis was used to analyse the electroencephalography (EEG) signal. K-nearest neighbor classifier was used for classification in this paper. The accuracy results of alpha (8-13 Hz), beta (13-30 Hz), combined alpha and beta (8-30 Hz) and combined theta, alpha, beta and gamma (4-40 Hz) frequency bands were compared. Overall, the percentage of accuracy was above 80%. The most suitable frequency bands for human EEG-based biometric identification in this experiment was the combined theta, alpha, beta, and gamma (4-40 Hz). The percentage of accuracy for this frequency band was the highest among the others which is 89.21%.

Poster presented at the International Association for the Study of Dreams, 34th Annual International Conference, June 16-20, 2017. The poster outlines a study on avatar animation of dreamer motor behavior from electromyography collected at the University of Texas, Austin, Cognitive Neuroscience Lab in 2016.

Electromyography (EMG) is a procedure for assessing and recording the electrical activity produced by skeletal muscles. Since the contracting skeletal muscles are greatly responsible for loading the bones and joints, information about the muscle EMG is important to gain knowledge about muscular-skeletal biomechanics.

Applications, Challenges, and Advancements in Electromyography Signal Processing provides an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. Presenting new results, concepts, and further developments in the field of EMG signal processing, this publication is an ideal resource for graduate and post-graduate students, academicians, engineers, and scientists in the fields of signal processing and biomedical engineering.

This book is intended for both mechanical and electronics engineers (researchers and graduate students) who wish to get some training in smart electronics devices embedded in mechanical systems. The book is partly a textbook and partly a monograph. It is a textbook as it provides a focused interdisciplinary experience for undergraduates that encompass important elements from traditional courses as well as contemporary developments in Mechatronics. It is simultaneously a monograph because it presents several new results and ideas and further developments and explanation of existing algorithms which are brought together and published in the book for the first time.

Ionic voltages produced as a result of electrochemical activity of special type of cells are called bioelectric potential. Bioelectric potentials are generated to nerve conduction, heart activity, heartbeat, muscle activity etc , The bioelectric signal produced in patient body are coordinated activity of large group of cells. Electroencephalography (EEG), Electrocardiography (ECG), and electromyography (EMG) systems measure brain, heart, Muscle activity. Measuring electric potentials on surface of active tissue. Nervous stimuli & muscle contractions can be identified by measuring the ionic current flow in body. This is consummate using a biopotential electrode. Frequency range of bioelectric signal is ECG amplitude is 50µV-5µV, EEG amplitude is 2µV-100µV, EMG amplitude is 20µV-5µV.

—Prosthetic hand control based on the acquisition and processing of surface electromyography signals (sEMG) is a well-established method that makes use of the electric potentials evoked by the physiological contraction processes of one or more muscles. Furthermore intelligent mobile medical devices are on the brink of introducing safe and highly sophisticated systems to help a broad patient community to regain a considerable amount of life quality. The major challenges which are inherent in such integrated system's design are mainly to be found in obtaining a compact system with a long mobile autonomy, capable of delivering the required signal requirements for EMG based prosthetic control with up to 32 simultaneous acquisition channels and – with an eye on a possible future exploitation as a medical device – a proper perspective on a low priced system. Therefore, according to these requirements we present a wireless, mobile platform for acquisition and communication of sEMG signals embedded into a complete mobile control system structure. This environment further includes a portable device such as a laptop providing the necessary computational power for the control and a commercially available robotic hand-prosthesis. Means of communication among those devices are based on the Bluetooth standard. We show, that the developed low cost mobile device can be used for proper prosthesis control and that the device can rely on a continuous operation for the usual daily life usage of a patient.

Appropriate cancellation of the baseline fluctuation (BLF)
is an important issue when recording EMG signals as it may degrade signal quality and distort qualitative and quantitative analysis. We present statistical
and filter-design approach for cancellation of the
BLF
based on several signal processing techniques used
sequentially. The methodology is to estimate the spectral
contents of the BLF, and then to use this estimation to
design a high pass Butterworth filter by using Bili
near Transformation that cancel the BLF present in the signal.
Two merit figures are devised for measuring the degree of BLF present in an EMG record. These figures are used to
compare both methods which naively consider the baseline
without any fluctuation i.e. constant potential shift.
Applications of the techniques on real and simulate
d EMG
signals show the superior performance of our approa
ch in
terms of both visual inspection and the merit figur
es.

—We investigate the problem of achieving robust control of hand prostheses by the Electromyogram (EMG) of transradial amputees in the presence of variable force levels, as these variations can have a substantial impact on the robustness of the control of the prostheses. We also propose a novel set of features that aim at reducing the impact of force level variations on the prosthesis controlled by amputees. These features characterize the EMG activity by means of the orientation between a set of spectral moments descriptors extracted from the EMG signal and a nonlinearly mapped version of it. At the same time, our feature extraction method processes the EMG signals directly from the time-domain to reduce computational cost. The performance of the proposed features is tested on EMG data collected from nine transradial amputees performing six classes of movements each with three force levels. Our results indicate that the proposed features can achieve significant reductions in classification error rates in comparison to other well-known feature extraction methods, achieving improvements of §6% to 8% in the average classification performance across all subjects and force levels, when training with all forces.

This work is done in order to develop a real-time simulation tool for the training of adjacent muscles at a wrist disarticulation level, where users can train muscle activation through the continuous practice of contractions and relaxations in the forearm, so that in the future the coupling of an artificial extension of the upper limb allows them to establish New relationships with the environment and meet your basic needs. The tool seeks to give a support to the rehabilitation processes from the places of residence of the users thus avoiding the transfer of people to healthcare centers.

Stroke merupakan gangguan yang mengakibatkan cacat fungsional otak
yang dapat berujung pada kecacatan bahkan kematian. Kecacatan yang ditimbulkan
oleh stroke tidak begitu saja sembuh, melainkan perlunya perawatan dan
rehabilitasi yang membutuhkan biaya yang mahal sehingga dibutuhkan alat bantu
rehabilitasi. Alat bantu rehabilitasi stroke yang dirancang sudah banyak, salah
satunya adalah berbentuk robot. Akhir-akhir ini alat bantu rehabilitasi sudah banyak
mulai menggunakan EEG dan EMG. Sayangnya, belum banyak penelitian yang
mendukung perkembangan alat bantu ini. EEG dan EMG adalah dua jenis biosignal pada bidang kesehatan yang merupakan nama lain dari gelombang otak dan
gelombang kontraksi otot. Gelombang ini hadir memberikan warna baru terhadap
pengembangan teknologi dalam berbagai bidang, terutama dalam bidang kesehatan.
Pemanfaatan terhadap dua bio-signal ini masih terus berlanjut dan bervariasi
jenisnya. Banyaknya minat penelitian terhadap electroencephalograpy dan
electromyography menarik banyak bidang yang bisa mengaplikasikan langsung dua
jenis bio-signal ini mulai dari, pengembangan game, teknologi sistem cerdas untuk
masa depan, hingga keperluan medis yang memudahkan dalam menganalisis
berbagai macam gangguan kesehatan dan rehabilitasi salah satunya adalah stroke.
Penellitian yang sudah lama dilakukan di Jepang untuk alat bantu rehabilitasi stroke
memanfaatkan EEG dan EMG yang difungsikan untuk menggerakkan alat
walaupun kondisi kaki dan tangan pasien masih lumpuh. Alat ini berbentuk robot
yang dikembangkan dengan model exoskeleton.
Metode yang digunakan untuk penelitian ini adalah analisis terhadap
gelombang yang dihasilkan oleh otak dan kontraksi otot. Setiap jenis gelombang
dikelompokkan terhadap aktivitas dan jenis bentuk tubuh yang berbeda. Data yang
didapat adalah hasil dari pengumpulan sampel. Setiap pengambilan sampel yang
dilakukan akan digolongkan berdasarkan karakteristik dan jenis bentuk tubuh orang
untuk mendapatkan hasil yang baik. Hasil yang didapatkan dianalisis mengenai
efektivitas gelombang EEG dan EMG sehingga dapat diimplementasikan
pada alat bantu rehabilitasi.
Diharapkan dari penelitian ini dapat berkembang menjadi alat bantu yang
bisa digunakan secara luas sebagai alternatif rehabilitasi bagi pasien pasca stroke.
Sehingga penelitian terhadap analisis EEG dan EMG dirasa sangatlah perlu sebagai
langkah awal berkembangnya alat bantu rehabilitasi stroke

Recent decades have seen a move toward evidence-based medicine to inform the clinical decision-making process with reproducible findings from high-quality research studies. There is a need for objective, quantitative measurement tools to increase the reliability and reproducibility of studies evaluating the efficacy of healthcare interventions, particularly in the field of physical and rehabilitative medicine. Surface electromyography (sEMG) is a non-invasive measure of muscle activity that is widely used in research but is under-utilized as a clinical tool in rehabilitative medicine. Other types of electrophysiological signals (e.g., electrocardiography, electroencephalography, intramuscular EMG) are commonly recorded by healthcare practitioners, however, sEMG has yet to successfully transition to clinical practice. Surface EMG has clear clinical potential as an indicator of muscle activation, however reliable extraction of information requires knowledge of the appropriate methods for recording and analyzing sEMG and an understanding of the underlying biophysics. These concepts are generally not covered in sufficient depth in the standard curriculum for physiotherapists and kinesiologists to encourage a confident use of sEMG in clinical practice. In addition, the common perception of sEMG as a specialized topic means that the clinical potential of sEMG and the pathways to application in practice are often not apparent. The aim of this paper is to address barriers to the translation of sEMG by emphasizing its benefits as an objective clinical tool and by overcoming its perceived complexity. The many useful clinical applications of sEMG are highlighted and examples provided to illustrate how it can be implemented in practice. The paper outlines how fundamental biophysics and EMG signal processing concepts could be presented to a non-technical audience. An accompanying tutorial with sample data and code is provided which could be used as a tool for teaching or self-guided learning. The importance of observing sEMG in routine use in clinic is identified as an essential part of the effective communication of sEMG recording and signal analysis methods. Highlighting the advantages of sEMG as a clinical tool and reducing its perceived complexity could bridge the gap between theoretical knowledge and practical application and provide the impetus for the widespread use of sEMG in clinic.

Analysis of Electromyographic signals (EMG) allows obtaining useful information to develop gesture recognition applications. In this paper we propose an FPGA-based wearable EMG gesture recognition system to support the communication of customized phrases for people with speech impairments. Machine learning algorithms can accurately identify gestures, usually relying in a large number of features or large training datasets. In this work, we focus on using a small number of features in the gesture classification process in order to reduce the need for computational power. Namely, we propose using only one feature, the Root Mean Square (RMS) signal value, and the KNN supervised classification algorithm. The system is evaluated in an DE10-Standard FPGA to demonstrate portability to wearable devices with limited hardware resources. Tests show that subjects only need three seconds per gesture to train the system, which avoids processing large amounts of data and improves user experience during the equipment setup. Furthermore, the accuracy of the system reaches 95% using only 2 seconds of data effectively maintaining both human and hardware resources low.

A myoelectric prosthetic arm can replace the missing arm that has been lost due to accident or diseases. This physical impairment limits the motor abilities of human arm. Our project aims to develop a cost effective arm using surface EMG electrodes. The muscle signals are collected using a sensor, processed and given to a microcontroller to rotate the servo motors accordingly. The magnitude of voltage signal decides the servo motor to be operated.

Interference or a crosstalk from nearby muscles is a classic problem in surface electromyographic recordings. Most studies of crosstalk diminution have only focused on blind source separation techniques and spatial filtering of simulated signals. The aim of this study was to perform an extraction of the activity of index and little finger extensor muscles from the electromyographic mixtures recorded with a surface electrode array. The motor unit action potential were detected in the mixtures and classified according to an inter-electrode delay. The signal-to-interference ratio performance of the proposed method was higher than performance of the beamformer and the frequency JADE algorithms. The findings show that creation of a muscle action potential propagation model, required by beamformer, could be problematic. The proposed algorithm uses a generalized model and shows higher performance.

An object detection system finds objects of the real world present either in a digital image or video, where the object can belong to any class of objects namely humans, cars, etc. In order to detect an object in an image or a video the system needs to have a few components in order to complete the task of detecting an object, they are a model database, a feature detector, a hypothesiser and a hypothesiser verifier. This paper presents a review of the various techniques that are used to detect an object, localise an object, categorise an object, extract features, appearance information, and many more, in images and videos.

Gender recognition is trivial for a physiotherapist, but it is considered a challenge for computers. The electromyography (EMG) and heart rate variability (HRV) were utilized in this work for gender recognition during exercise using a stepper. The relevant features were extracted and selected. The selected features were then fused to automatically predict gender recognition. However, the feature selection for gender classification became a challenge to ensure better accuracy. Thus, in this paper, a feature selection approach based on both the performance and the diversity between the two features from the rank-score characteristic (RSC) function in a combinatorial fusion approach (CFA) (Hsu et al.) was employed. Then, the features from the selected feature sets were fused using a CFA. The results were then compared with other fusion techniques such as naive bayes (NB), decision tree (J48), k-nearest neighbor (KNN) and support vector machine (SVM). Besides, the results were also compared with previous researches in gender recognition. The experimental results showed that the CFA was efficient and effective for feature selection. The fusion method was also able to improve the accuracy of the gender recognition rate. The CFA provides much better gender classification results which is 94.51% compared to Barani's work (90.34%), Nazarloo's work (92.50%), and other classifiers.

Presented is a simulation study highlighting the advantages of electrical impedance tomography (EIT) brain function monitoring compared to the electrical potential measurement (sEEG) while using depth electrodes to localize the epileptogenic zone (EGZ). One of the biggest problem of sEEG is its " overshadowing " by the close neural activity, known as the " tunnel-vision " effect. Based on our model, we argue that sEEG-EIT is less susceptible to tunnel-vision due to its higher separation ability of different directions of arrival (DOAs) of neural activity. Moreover, since the number of degrees of freedom for such measurement scales as the square of the number of electrodes, the benefit of impedance-based measurement will increase as the electrical models of the head will improve. In our simulation studies we explain why the regular modeling of the electrical activity by dipolar current sources perpendicular to the cortex does not explain the " tunnel-vision " and propose another realistic model to explain this effect. We show the advantage in the reconstruction of the neural activity using sEEG-EIT instead of sEEG. We also show that compared to regular sEEG, the gain matrix of sEEG-EIT is better conditioned by about 2.2 dB and has a significantly higher numerical rank.

This paper presents a comparison between two Artificial Neural Network (ANN) approaches, namely, Multilayer Perceptron (MLP) and Radial Basis Function (RBF) networks, in flood forecasting. The basic difference between the two methods is that the parameters of the former network are nonlinear and those of the latter are linear. The optimum model parameters are therefore guaranteed in the latter, whereas it is not so in the more popularly adopted former approach. The two methods are applied to predict water levels at stations in an experimental drainage basin and in a major river in China during storm periods. The RBF network based models give predictions comparable in accuracy to those from the MLP based models. It is also observed that the RBF approach requires less time for model development since no repetition is required to reach the optimum model parameters.

The electromyographic signal is used in lots of bioengineering studies for controlling systems and to give information about muscle behavior. In order to use this signal that comes from muscle activities this work proposes to develop a platform for electromyographic signal acquisition and analysis. This platform allows to conditioned the signal and analyze its behavior. Then it is an important device to support a large range of researches as prosthesis control system and devices based on functional electrical stimulation (FES). The platform is portable what makes it adequate to use in different places as hospital, rehabilitation centers and laboratories. The signal analyses response is friendly and the platform allows to use the signal to control some devices through the out signal port. This equipment demonstrates to be suitable for various researches studies in the areas of rehabilitation, physiotherapy, sport physiology and other areas related to biomedicine and bioengineering.

Summer Internship Project report while working under the supervision of Prof. Jayanta Mukhopadhyay, Computer Science Engineering Department, IIT Kharagpur and Prof. Manjunatha Mahadevappa, School of Medical Science and Technology, IIT Kharagpur in the summer of 2013.

The motion of human body is complex but perfect and integrated effort of brain, nerves and muscles. Exoskeleton is a promising idea for human rehabilitation of the lower limb that is weak enough to move. EMG signal contains the information of human movement and can be considered as one of the most powerful input to exoskeleton controller. In this research, the activity of the lower limb muscles that are responsible for human sit to stand and stand to sit movement has been studied. In this regard, the activities of three muscles viz. rectus femoris, vastus lateralis and biceps femoris have been observed and recorded to perceive their activation pattern. The experimental results show that the maximum voltage of vastus lateralis at activation moment is greater or equal to +0.1 mV or lesser or equal to -0.1 mVduring sit to stand and stand to sit movement whereas same throughput was found for biceps femoris during sit to stand and for rectus femoris during stand to sit movement only. © 2013 IEEE.

http://ieeexplore.ieee.org/ielx7/6720515/6735086/06735124.pdf?tp=&arnumber=6735124&isnumber=6735086

Salat is the practice of formal prayer for Muslims. It involves with both spiritual and physical acts. This paper presents electromyography (EMG) output during the "takbeeratul-ihram (TI)", one of the postures in Salat and stretching exercise. Comparisons on EMG outputs between TI and stretching exercise are discussed. Ten healthy participants (five males and five females) were recruited for the investigation. The EMG signals were recorded from back scapula, upper trapezius, biceps brachii and pectoralis major muscles. The results showed that higher EMG signals in pectoralis major and biceps during performing the stretching posture. However, for TI posture, the EMG activities are higher in back scapula and upper trapezius muscles. © 2012 IEEE.

ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6211603

In recent years, distribution generation has been expanding considerably. While interfacing the distributed generation to utility grid, power electronic devices are widely used to maximize the power transfer from DG to grid. The main aim of the power system is to ensure quality power to the customers. Thus, passive harmonic filters such as LCL filters are often preferred to mitigate the harmonics in Voltage Source Converters that are interconnected to the grid. The LCL filters however result in a resonant frequency that needs to be damped. The thesis proposes a current control technique to damp the resonant frequency in the grid-connected VSC with LCL filter. The harmonics in the system are analysed through the Fast Fourier Transform approach and the Total Harmonic Distortion of the VSC with and without filters is identified. The entire work is carried out in MATLAB/Simulink and resonance induced by the filters is damping by implementing current control techniques.

Image segmentation is to find the desired object presented in the image. Gray Level Local Variance (GLLV), Gray Level Local Entropy (GLLE), Gray Level Spatial Correlation (GLSC) are different 2D histogram methods used to do the segmentation using Tsallis Entropy. In this paper, visible images of Plasma of TOKAMAK are used to extract the desired bright spots from images using different 2D histogram Segmentation Methods, which can further be used to find the position of the plasma. Due to unavailability of ground truth image, unsupervised parameter Uniformity Value is used as an evaluation parameter. Result shows that GLSC based Segmentation provides better result in terms of Uniformity Value compared to the other methods.

✅ WEBINAR "TECNOLOGÍA Y SALUD"
✅ Agenda:
✅ Introducción
▷ EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
Asanza, V., Pelaez, E., & Loayza, F. (2017, October).
▷ EEG signal clustering for motor and imaginary motor tasks on hands and feet. In Ecuador Technical Chapters Meeting (ETCM), 2017 IEEE (pp. 1-5). IEEE.
▷ EMG Signal Processing with Clustering Algorithms for Motor Gesture Tasks
Asanza, V., Peláez, E., Loayza, F., Mesa, I., Díaz, J., & Valarezo, E. (2018, October).
▷ EMG Signal Processing with Clustering Algorithms for motor gesture Tasks. In 2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM) (pp. 1-6). IEEE.
✅ Resultados Obtenidos
▷ C. Cedeño Z., J. Cordova-Garcia, V. Asanza A., R. Ponguillo and L. Muñoz M., "k-NN-Based EMG Recognition for Gestures Communication with Limited Hardware Resources," 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Leicester, United Kingdom, 2019, pp. 812-817.
▷ 2019: Artificial Neural Network based EMG recognition for gesture communication (InnovateFPGA)
✅ Preguntas

Lucid dreamers have been communicating with the outside since the 1970's. They have used their eyes and hands in the dream to send a " Hello " to all of us in the " real " world. Some have even attempted Morse code as a way to send a more detailed message from their lucid dream.

Yet mankind's earliest communication tool, spoken language, has never been used to communicate, mostly because we cannot speak while dreaming—speech muscles are largely paralyzed during REM dreams, at least from what can be observed. That may be about to change.

In the near future, lucid dreamers may be able to provide a first-hand description of their dreams while in the dream. Though we cannot observe someone speaking in a dream, technology can still detect nerve impulses sent to speech muscles and convert these impulses into sounds, words and sentences—at least theoretically.

Sound spatialisation is an important component in interactive per- formances as well as in game audio and virtual or mixed reality systems. HCI practices are increasingly focused on creating a natural user experience and embodied interaction through gestu- ral control. Body movements that coincide with sounds consist of both performed ‘sound producing‘ gestures, and ancillary and communicative movements. Thus, different gestural typologies may relate to the same audio source. Furthermore, gestures may depend on the context in which they have been expressed; in other words, they can carry different semantic or semiotic meanings in relationship to the situation and environment or reality in which they have been enacted. In order to explore these research themes, we are developing gSPAT: a software and hardware system able to drive live sound spatialisation for interactive audio performance using gestural control based on human-meaningful gesture-sound relationships. The ultimate aim is to provide a highly natural and musically expressive sound spatialisation experience for the per- former. Here we describe three experiments conducted to explore the possible directions for the future of gSPAT’s development. The tests employ a range of practice-based and ethnographic research methods to establish applicability, naturalness and usability across a range of approaches to the interaction design of the system.

The motion of human body is complex but perfect and integrated effort of brain, nerves and muscles. Exoskeleton is a promising idea for human rehabilitation of the lower limb that is weak enough to move. EMG signal contains the information of human movement and can be considered as one of the most powerful input to exoskeleton controller. In this research, the activity of the lower limb muscles that are responsible for human sit to stand and stand to sit movement has been studied. In this regard, the activities of three muscles viz. rectus femoris, vastus lateralis and biceps femoris have been observed and recorded to perceive their activation pattern. The experimental results show that the maximum voltage of vastus lateralis at activation moment is greater or equal to +0.1 mV or lesser or equal to -0.1 mVduring sit to stand and stand to sit movement whereas same throughput was found for biceps femoris during sit to stand and for rectus femoris during stand to sit movement only. © 2...

Electromyography (EMG) is widely used throughout the world for different study such as clinical diagnosis and for movement analysis. One of the applications of EMG is in the development of myoelectric prosthesis. It is intended by all the biomechanics engineer in order to provide better living to the amputees since the current prosthesis are limited. It is principally operated based on the EMG signal generated from muscle contraction. Therefore, the challenge to develop myoelectric prosthesis devices begins with the challenging part to understand completely the principles of elec-tromyography (EMG). Knowledge of EMG could lead the researcher to apply the signal correctly. This conceptual paper will provide better understanding and framework in process to develop such intelligent prosthesis.

— Muscle Fiber Conduction Velocity (MFCV) can be calculated from the time delay between the surface electromyo-graphic (sEMG) signals recorded by electrodes aligned with the fiber direction. In order to take into account the nonstationarity during the dynamic contraction (the most daily life situation) of the data, the proposed methods have to consider that the MFCV changes over the time, which induces non-constant time delays. In this study, the effect of the nonstationarity (change of Power Spectral Density) of the sEMG signals on the performance of the time-varying delay estimators recently developed by our group is investigated. This study presents a set of approaches for instantaneous delay estimation from two-channels EMG signals. The performance of the estimators is evaluated and compared through Monte-Carlo simulations in order to determine if their performance statistics are sufficient for practical applications.