Advanced analysis and interpretation of the EMG signal

How to interpret the EMG signal?

The maximal force capacity of a muscle unit depends on the average cross-sectional area of the muscle fibers (μm2), the specific force of the fibers (mN/μm2), and the innervation number. Of these three factors, the most significant is the number of fibers in the muscle unit. Therefore, the weakest muscle units

have the lowest innervation numbers, whereas the strongest muscle units comprise the greatest number of muscle fibers. 

Force production in a muscle is regulated by two main mechanisms, namely: 

• The recruitment of additional MUs
•  The increase in firing rate of the already active MUs

These two mechanisms are present in different proportions in different muscles. As for the developed force, also the amplitude of the surface EMG signal depends on both the number of active MUs and their firing rates. Since both EMG amplitude and

force increase as a consequence of the same mechanisms, it is expected that muscle force may be estimated from surface EMG analysis. 

The relationship between EMG amplitude (arv-average rectified value) and force varied, depending on the recruitment strategy and the uniformity among the peak firing rates of the different Mus.

When discussing the issue of the relation between EMG amplitude and force, a number of other factors should be taken into account. First, the surface EMG amplitude depends strongly on the electrode location.

For locations in which EMG amplitude is very sensitive to small electrode displacements, it is expected that the relation between EMG and force may be poorer than in other locations.

Emg signal processing

Processing the signal means to apply algorithms to extract parameters or features to be used for some purpose, such as signal classification or quantification of changes.

A raw sEMG signal is usually processed to mainly extract information concerning the “amplitude” of the signal: 

• RMS (Root Mean Square): Provides an overall measure of the signal’s strength/trend.
• ARV (Average Rectified Value): Represents the average amplitude of the rectified signal.
• LE (Linear Envelope): Captures the profile of muscle activity
• Power Spectral Density: Analyzes the frequency content of the signal using Fourier or autoregressive methods. This helps identify muscle fatigue through changes in Mean and Median Frequency (MNF & MDF)

These parameters provide information, respectively, about the muscle contraction “strength” and about the frequency content of the signal, which is, in turn and in certain conditions, one of the many myoelectric manifestations of muscle fatigue. These parameters are well known in the field and clinically used in movement and rehabilitation science.

In the time domain, the dominant changes in the single-channel sEMG are: the modulation of the signal standard deviation (RMS) or of the ARV and the spectral changes due to muscular effort and/or fatigue.

As a muscle effort increases, the signal strength (or amplitude) grows. 

The RMS (Root Mean Square) is a value that can give you a global/general information about the performance/trend of the EMG signal.

From EMG signal is possible to see the envelope to analyze the profile of muscles activities. This analysis is available in our software EMG and Motion Tools, 

It is possible to detect the muscle contractions with the Onset Detection analysis, always available in our software EMG and Motion Tools.

To analys the muscle contraction in frequency domain, you can apply the FFT analysis. This analysis is available in our software EMG and Motion Tools, you can also export, copy to clipboard or to the report the results of the FFT analysis.

Spectral analysis of the surface EMG signal has been extensively applied for the study of muscle fatigue, both in voluntary and electrically elicited contractions. The preferred application of these techniques has

been the analysis of isometric constant force, short-duration contractions of medium–high level. It is now accepted that relative changes in EMG spectral variables reflect fatigue, with the possibility of detecting age (or other)-related differences in muscle fiber composition. 

Applications of spectral analysis of the EMG signal for fatigue assessment during non constant force and dynamic contractions have been proposed in more recent years, together with advances in spectral estimation techniques based on time–frequency representations.

In different application as the rehabilitation, can be helpful to understand the maximum voluntary contraction value, with MVC analysis, in EMG and Motion Tools, it is possible to understand the max value and compare

the signal to this value. The MVC analysis is the base to work with the biofeedback viewer, a tool available in our software.

Our software, EMG and Motion Tools, offers various functionalities for EMG signal analysis:

• Envelope Analysis: Visualize the profile of muscle activity using the extracted envelope.
• Onset Detection: Identify the exact moments of muscle contractions.
• FFT (Fast Fourier Transform) Analysis: Analyze the signal in the frequency domain to assess muscle fatigue.
• MVC (Maximum Voluntary Contraction) Analysis: Determine the maximum voluntary contraction force and compare subsequent signals to this value. This serves as the baseline for biofeedback analysis, another tool available in our software.