17th Annual Meeting of the Biofeedback Foundation of Europe Presentazione: La metodologia dell’ elettromiografia di superficie (SEMG). It is used diagnostically by gait laboratories and by clinicians trained in the use of biofeedback or ergonomic assessment. EMG is also used in. Monitoraggio neurofisiologico · Dolore · Stimolazione elettrica · Elettromiografia · PNEUMOLOGIA · Polisonnografia · Biofeedback · MED. FISICA E RIAB.

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EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and bilfeedback ways of understanding the signal and its nature.

Techniques of EMG signal analysis: detection, processing, classification and applications

We further point up biofsedback of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures.

This knowledge will help them develop more powerful, flexible, and efficient applications. Biomedical signal means a collective electrical signal acquired from any organ that represents a physical variable of interest.

This signal is normally a function eletyromiografico time and is describable in terms of its amplitude, frequency and phase. The EMG signal is a biomedical signal that measures electrical currents generated in muscles during its contraction representing neuromuscular activities. Hence, the EMG signal is a complicated signal, which is controlled by the nervous system and is dependent on the anatomical and physiological properties of muscles.

EMG signal acquires noise while traveling through elertromiografico tissues. Moreover, the EMG detector, particularly if it is at the surface of the skin, collects signals from different motor units at a time which may generate interaction of different signals.

Detection of EMG signals with powerful and advance methodologies is becoming a very important requirement in biomedical engineering. The main reason for the interest in EMG signal analysis is in clinical diagnosis and biomedical applications. The field of management and rehabilitation of motor disability biofeedbac, identified as one of the important application areas.

Once appropriate algorithms and methods for EMG signal analysis are readily available, the nature and characteristics of the signal can be properly understood and hardware implementations can be made for various EMG signal related applications. So far, research and extensive efforts biofeebdack been made in the area, developing better algorithms, upgrading existing methodologies, improving detection techniques to reduce noise, and to acquire accurate EMG signals.

Elehtromiografico hardware implementations have been done for prosthetic hand control, grasp recognition, and human-machine interaction. It is quite important to carry out an investigation to classify the actual problems of EMG signals analysis and justify the accepted measures. Bioeedback technology of EMG recording is relatively new. There are still limitations in detection and characterization of existing nonlinearities in the surface electromyography sEMG, a special technique for studying muscle signals signal, estimation of the phase, elttromiografico exact information due to derivation from normality 12 Traditional system reconstruction algorithms have various limitations and considerable computational complexity and many show high variance 1.

Recent advances in technologies of signal processing and mathematical models elettromiografioc made it practical to develop advanced EMG detection and analysis techniques. Various mathematical techniques and Artificial Intelligence AI have received extensive attraction. Mathematical models include wavelet transform, time-frequency approaches, Fourier transform, Biofeedvack Distribution WVDstatistical measures, and higher-order statistics.

Genetic Algorithm GA has also been applied in evolvable hardware chip for the mapping of EMG inputs to desired hand actions. Wavelet transform is well suited biofeexback non-stationary signals like EMG. Time-frequency approach using WVD in hardware could allow for a real-time instrument that can be used for specific motor unit training in biofeedback situations.


The bispectrum or third-order spectrum has the advantage of biofedback Gaussian noise. This paper firstly gives a brief explanation about EMG signal and a short historical background of EMG signal analysis. This is followed by highlighting the up-to-date detection, decomposition, processing, and classification methods of EMG signal along with a comparison study.

Finally, some hardware implementations and applications of EMG have been discussed. EMG stands for electromyography. It is the study of muscle electrical signals. EMG is sometimes referred to as myoelectric activity. Muscle tissue conducts electrical potentials similar to the way nerves do and the name given to these electrical signals is the muscle action potential.

Surface EMG is a method of recording the information present in these muscle action potentials. When detecting and recording the EMG signal, there are two main issues of concern that influence the fidelity of the signal.

The first is the signal-to-noise ratio. That is, the ratio of the energy in the EMG signals to the energy in the noise signal. In general, noise is defined as electrical signals that are not part of the desired EMG signal. The other issue is the distortion of the signal, meaning that the relative contribution of any frequency component in the EMG signal should not be altered.

Two types of electrodes have been used to acquire muscle signal: When EMG is acquired from electrodes mounted directly on the skin, the signal is a composite of all the muscle fiber action potentials occurring in the muscles underlying the skin. These action potentials occur at random intervals. So at any one moment, the EMG signal may be either positive or negative voltage. Individual muscle fiber action potentials are sometimes acquired using wire or needle electrodes placed directly in the muscle.

The combination of the muscle fiber action potentials from all the muscle fibers of a single motor unit is the motor unit action potential MUAP which can be detected by a skin surface electrode non-invasive located near this field, or by a needle electrode invasive inserted in the muscle 3. Equation 1 shows a simple model of the EMG signal:. The signal is picked up at the electrode and amplified.

Typically, a differential amplifier is used as a first stage amplifier. Additional amplification stages may follow. Before being displayed or stored, the signal can be processed to eliminate low-frequency or high-frequency noise, or other possible artifacts. Frequently, the user is interested in the amplitude of the signal. Consequently, the signal is frequently rectified and averaged in some format to indicate EMG amplitude.

The nervous system is both the controlling and communications system of the body.


This system consists of a large number of excitable connected cells called neurons that communicate with different parts of the body by means of electrical signals, which are rapid and specific. The nervous system consists of three main parts: The neurons are the basic structural unit of the nervous system and vary considerably in size and shape.

Neurons are highly specialized cells that conduct messages in the form of nerve impulses from one part of the body to another. A muscle is composed of bundles of specialized cells capable of contraction and relaxation. The primary function of these specialized cells is to generate forces, movements and the ability to communicate such as speech or writing or other modes of expression.

Muscle tissue has extensibility and elasticity. It has the ability to receive and respond to stimuli and can be shortened or contracted.

Muscle tissue has four key functions: Three types of muscle tissue can be identified on the basis of structure, contractile properties, and control mechanisms: The EMG is applied to the study of skeletal muscle.


The skeletal muscle tissue is attached to the bone and its contraction is responsible for supporting and moving the skeleton. The contraction of skeletal muscle is initiated by impulses in the neurons to the muscle and is usually under voluntary control.

Skeletal muscle fibers are well-supplied with neurons for its contraction. This particular type of neuron is called a “motor neuron” and it approaches close to muscle tissue, but is not actually connected to it.

One motor neuron usually supplies stimulation to many muscle fibers. The human body as a whole is electrically neutral; it has the same number of positive and negative charges. But in the resting state, the nerve cell membrane is polarized due to differences in the concentrations and ionic composition across the plasma membrane. A potential difference exists between the intra-cellular and extra-cellular fluids of the cell.

In response to a stimulus from the neuron, a muscle fiber depolarizes as the elettrojiografico propagates along its surface and bioofeedback fiber twitches. This depolarization, accompanied by a movement of ions, generates an electric field near each muscle fiber.

The EMG signal appears random in nature and is generally modeled as a filtered impulse process where the MUAP is the filter and the impulse process stands for the neuron pulses, often modeled as eleftromiografico Poisson process 3. The document informs that highly specialized muscle of the electric ray fish generates electricity 3.

Galvani, where the author showed that electricity could initiate muscle contractions 4. Six decades later, inDubios-Raymond discovered that it was also possible to record electrical activity during a voluntary muscle contraction. Elettromiogtafico first recording of this activity was made by Marey inwho also introduced the term electromyography 5.

InGasser and Erlanger used an oscilloscope to show the electrical signals from muscles. Because of the stochastic nature of the myoelectric signal, only rough information could be obtained from its observation. Elettrojiografico capability of detecting electromyographic signals improved steadily from the s through the s and researchers began to use improved electrodes more widely for the study of muscles 1.

Clinical use of surface EMG for the treatment of more specific disorders began in the s. Hardyck and his researchers were the first practitioners to use sEMG 5. In the early s, Cram and Steger introduced a clinical method for scanning a variety of muscles using an EMG sensing bipfeedback 5. It is not until the middle of the s that integration techniques in electrodes had sufficiently advanced to allow batch production of the required small and lightweight instrumentation and amplifiers.

At present a number of suitable amplifiers are commercially available. In the early s, cables became available which produce artifacts in the desired microvolt range. During the past 15 years, research has resulted in a better understanding of the properties of surface EMG recording. In recent years, surface electromyography is increasingly used for recording from superficial muscles in clinical protocols, where intramuscular electrodes are used for deep muscle only 24.

There are many applications for the use of EMG. EMG is used clinically for the diagnosis of neurological and neuromuscular problems. It is used diagnostically by gait laboratories and by clinicians trained in the use of biofeedback or ergonomic assessment.

EMG is also used in many types of research laboratories, including those involved in biomechanics, motor control, neuromuscular physiology, movement disorders, postural control, and physical therapy. EMG signals acquire noise while traveling through different tissue. It is important to understand the characteristics of the electrical noise. Electrical noise, which will affect EMG signals, can be categorized into the following types:.