**2.3. Treating the EMG signal**

Computational Intelligence in Electromyography Analysis – 98 A Perspective on Current Applications and Future Challenges

**2. Theory** 

healthy improvement.

**2.2. Time and frequency domain** 

to he/she thinks about it by himself or herself, and apply it properly.

**2.1. The importance of electromyography in cyclic exercises** 

not be the best way to pre-process the EMG signal. Other current concern, in EMG signal pre-processing, is about the use of the total signal against evaluation only the burst-time segments of the signal. Those concerns are explained and analyzed along this chapter. In an epistemological language, we take a more critic look into the EMG signal processing. We hope the reader also to have the same look, not only into the results and conclusions, but also, into methods and thoughts, since the intention herein is not to bring an irrefutable true, but the real intention is to discuss and point out valuable arguments for the reader in order

Cyclic exercises correspond to modalities such as bicycle, running, walking and swimming. Inside those we can already imagine a lot of different sports with a great repercussion over the media, a few examples include: street bike, mountain bike and tour, like the famous Tour of France; marathon, 400 meters, race walking and putting in just one thing, the triathlon. You may notice that the swimming sports are not exemplified above, it's because it is still hard to access the muscle activity through electromyography in those sports due to the environment where they happens. This discussion was set aside for our future work

So, keeping in mind that some of the most important sports have a cyclic dynamic as characterization, for the evolution of them, new technologies need to be able to help the coaches and physical trainers. Nowadays individual time-trial sports are reaching world records that we would never imagine, and every new record is followed by a technology behind it helping in training or even during the task if is not prohibited. To be able to access the muscle activity with a good reliability in different moments of the exercise could give us the weak and the strong moments of one athlete during the task, and allow us to create the better strategy and also create new training cycles that can improve the weakness. It is worth noting the electromyography is useful to access not only muscle activity -- in order to enhance the performance in sports --, but also be deployed in exercises evaluation aiming

Talk about frequency domain is talk about: how many times a event occurs in a time space, in this way, to use this component we need transform the signal in different points presented in a frequency spectrum capable to show us the energy of cue obtained in the determined muscle. This energy in the most part of the time appears represented in some bands, where your intensity and duration has more amplitude. To find and use the spectrum, we must find a source that gives us the possibility to produce this figure, when sometimes the Fast Fourier Transform proves as algorithm in a simple calculus to find discrete signals. A series of recommendations are proposed to this technique, since the establishment of sample number, duration intervals, window apply and many aspects as Now you already know about how the domains work and how to use them for different analysis depending on the applications necessity. During the subchapter "Analysis of the EMG signal" we hope it became clear that we have some procedures until the real signal is accessed, especially without noises. The raw signal can already give us some information, like the muscle innervations or even the change in the signal size. Depending of the intention, these qualitative variables can be very useful. An easy and good way to simple control some noises when there is no intention of further computer treatment to remove it, is to be sure to have a good baseline, meaning that the line that should appear at the EMG signal must be as close as it can to zero when the muscle with the electrode connected is not in contraction. That doesn't mean that when the muscle starts to contract the signal that will appear will only be from the muscle activity, especially in dynamic contractions. There are three main differences in noises on static contractions and dynamic ones, they are: the nonstationarity of the signal for the constant contraction and relaxing of the muscle, the change of the electrode distance relative to the origin of the action potential and the changes in the conductivity of the tissues properties [10].

A better way to understand what a noise is, is looking at it, the figure 1 under is an EMG signal with a closer look in the burst moment. Notice that the areas surrounded with black circles have a peculiar difference, it has a horizontal straight shaped line, which means that those parts don't have a corresponding negative part, and so, it is considered a noise. Of course in this same image you can find some more of those, not only the surrounded ones, but the intention here is only to show how a noise appears inside an EMG signal.

When the signal appears to us in the computer screen those details are impossible to see without a zoom look. So, lets talk now about how the treatments can influence in the signal value.

Influence of Different Strategies of Treatment Muscle Contraction

and Relaxation Phases on EMG Signal Processing and Analysis During Cyclic Exercise 101

With the intention of clearing the EMG signal, and make it more reliable and truthful, some computer processes are used before analyzes, they are: Filtration, Rectification and Smoothing. The image below shows the same signal from raw until the smoothed in order to obtain the RMS values for the vastus lateralis muscle in an exercise in 100% of the maximal watts in a cicloergometer during 60 seconds. Those process, expecially the Filtration and the Smoothing has the purpose of giving us the possibility to evaluate only the signal coming from the muscular contraction, without mechanical or electromagnetic

In a first moment the filtration occurs when the signal is been collected. With the objective to avoid interferences the EMG signal passes through a 50 to 60 Hz filter (notch filter), if it's necessary. This filter already starts rejecting the frequency band of 60 Hz once that in this band is where the ambient interferences like pressure appear, arrangement or closer apparatus. In a second moment, the EMG signal must pass through a pass-band; this passband frequency must be decided by the analyzer, once it can depend on the intentions of the study. Normally, this frequency is fixed between 20 and 450 Hz, because normally 80% of the muscular energy is concentrated [12-13]. But, as said before, it is a free choice for the user, once that this frequency can differ from muscle to muscle, so, it's important for the user to know exactly the band of the muscle that is been assessed to make sure that the passband will cut off only the signal that doesn`t belong to that muscle, and at the same time guarantee as precisely as it can that it won't let noises get inside the signal. Basically, it limits the signal inside a previous decided range to maintain it inside the muscle activation

The visual difference between the raw and the filtrated signal can be really hard to notice especially when the collected process is well cared, however, if we take a rigorous look to both of them, the difference will appear to our eyes, but remembering that the main reason

This procedure has the purpose to turn all the signal values integrative, submitting them to the cut of all negative values, that means, to delete the values that are under the baseline, or to turn all this negative values to positive adding the values, making them integrative. The second option is more recommended if the intention is to achieve the total muscle signal, if you cut off the negative part, half of the signal will be lost, so turning all of them positive is a more used and more interesting when it comes to final results. This procedure doesn't affect the signal noises like the filtration and the smoothing, which will be explained in sequence. However it is still recommended and made part of the studies involving this

This procedure is simple, and it can be easily understood by the figure above. Note that until the filtration moment the signal had both positive and negative side in the burst

chapter, so it's important for the reader to know how we used and what it means.

of using those treatments is to obtain the quantitative values of the signal.

interferences [2,11].

*2.3.1. Signal filtering* 

site.

*2.3.2. Signal rectification* 

**Figure 1.** Noises in the EMG signal

Note the figure 2 under, pay even more attention to the baseline in the raw signal, it is close to zero because it almost creates a straight line, considering that the muscle in this case is the vastus lateralis in a bike-like exercise, we can imagine that in the beginning of the exercise he is not much triggered, probably because the recto femuralis is doing almost all the job, but as time goes and also the exercise, its starts to have stronger signal, so we can imagine that the other muscles, like the recto femuralis is entering in fatigue process, so the vasto lateralis as a co-worker has to get part of this charge in order to maintain the exercise, that is the kind of qualitative analyze that was told before, without even knowing the values numbers, we can visually access an ideia about the use of vasto lateralis in a cicliergometer exercise.

**Figure 2.** EMG signal process recommended. Green: The raw signal, no treatment was applied until this moment; Red: Filtrated signal, a limit was created for the signal, excluding everything out of it; Blue: Rectified signal, all negative values were transformed in positive ones and added; Purple: the smoothed signal, a linear enveloped was created and the extreme parts of the signal was excluded; Black: The RMS values after all the treatments.

With the intention of clearing the EMG signal, and make it more reliable and truthful, some computer processes are used before analyzes, they are: Filtration, Rectification and Smoothing. The image below shows the same signal from raw until the smoothed in order to obtain the RMS values for the vastus lateralis muscle in an exercise in 100% of the maximal watts in a cicloergometer during 60 seconds. Those process, expecially the Filtration and the Smoothing has the purpose of giving us the possibility to evaluate only the signal coming from the muscular contraction, without mechanical or electromagnetic interferences [2,11].

### *2.3.1. Signal filtering*

Computational Intelligence in Electromyography Analysis – 100 A Perspective on Current Applications and Future Challenges

**Figure 1.** Noises in the EMG signal

Black: The RMS values after all the treatments.

Note the figure 2 under, pay even more attention to the baseline in the raw signal, it is close to zero because it almost creates a straight line, considering that the muscle in this case is the vastus lateralis in a bike-like exercise, we can imagine that in the beginning of the exercise he is not much triggered, probably because the recto femuralis is doing almost all the job, but as time goes and also the exercise, its starts to have stronger signal, so we can imagine that the other muscles, like the recto femuralis is entering in fatigue process, so the vasto lateralis as a co-worker has to get part of this charge in order to maintain the exercise, that is the kind of qualitative analyze that was told before, without even knowing the values numbers, we can

visually access an ideia about the use of vasto lateralis in a cicliergometer exercise.

**Figure 2.** EMG signal process recommended. Green: The raw signal, no treatment was applied until this moment; Red: Filtrated signal, a limit was created for the signal, excluding everything out of it; Blue: Rectified signal, all negative values were transformed in positive ones and added; Purple: the smoothed signal, a linear enveloped was created and the extreme parts of the signal was excluded;

In a first moment the filtration occurs when the signal is been collected. With the objective to avoid interferences the EMG signal passes through a 50 to 60 Hz filter (notch filter), if it's necessary. This filter already starts rejecting the frequency band of 60 Hz once that in this band is where the ambient interferences like pressure appear, arrangement or closer apparatus. In a second moment, the EMG signal must pass through a pass-band; this passband frequency must be decided by the analyzer, once it can depend on the intentions of the study. Normally, this frequency is fixed between 20 and 450 Hz, because normally 80% of the muscular energy is concentrated [12-13]. But, as said before, it is a free choice for the user, once that this frequency can differ from muscle to muscle, so, it's important for the user to know exactly the band of the muscle that is been assessed to make sure that the passband will cut off only the signal that doesn`t belong to that muscle, and at the same time guarantee as precisely as it can that it won't let noises get inside the signal. Basically, it limits the signal inside a previous decided range to maintain it inside the muscle activation site.

The visual difference between the raw and the filtrated signal can be really hard to notice especially when the collected process is well cared, however, if we take a rigorous look to both of them, the difference will appear to our eyes, but remembering that the main reason of using those treatments is to obtain the quantitative values of the signal.
