## 3 Results and Discussion
### Signal Characterization  
Figure 13 exemplifies a pre-processing stage when artifacts were removed from the signal for one of the animals in the climatic chamber (Figure 13a) and one unrestrained animal (Figure 13b). The proportion of artifact-free data in relation to the original data seen in Figure 13 was observed for most of the database. In all the acquisitions, at least one artifact-free section could be selected. Figure 14a contains the PSD for the artifact-free signal section in Figure 13a, while Figure 14b shows the PSD for the second artifact-free signal segment in Figure 13b. We were not able to establish a direct relationship between enthalpy and any specific behavior observed during the experiments (eating, drinking water, chewing, standing, lying down, or walking). The discomfort experienced by animals in the cage overlapped with any other behavior. This reflected even on the duration of artifact-free epochs in the signal (less than 4 s for all the animals). Figure 15 illustrates STFT for the second artifact-free signal segment in Figure 13b. We were not able to associate the observed frequencies in the group with any specific animal behavior, such as eating, drinking water, chewing, standing, lying down, or walking, mainly because of the amount of data that was acquired. However, the spectrogram clearly showed the predominant frequencies in cattle signals (Figure 15). As expected, the delta wave exhibited high amplitudes between 1 and 3 Hz and was particularly prominent between 1 and 2 Hz. These frequency values might potentially correspond to the delta wave or arise as a result of the rumination process [67]. The electromyographic signal has amplitudes in the range of mV, while the brain signal presents amplitudes in the range of mV. Considering this difference, in Figure 13, the muscular movements might be related to segments of \"scale clipping\". These segments were not used for processing, but it is still possible that there could be some interference in the selected segments for processing,  
Figure 13: Artifact removal. (**a**) Climatic chamber. (**b**) Pasture. Original signal is in grey and selected signal epochs in black. which could contribute to the presence of frequencies in the range of 1-3 Hz observed in Figure 15.  
There were also well-delimited regions between 4 and 7 Hz (Theta), 7 and 12 Hz, (Alpha), 12 and 30 Hz (Beta), and above 30 Hz (Gamma). The observed frequencies still agreed with the observations of [PERSON] et al. (2018) and [PERSON] et al. (2018). This methodology allowed us to acquire brain signals from animals that could move freely while minimizing the stress generated by confinement. It also allowed the acquisition to occur noninvasively. However, the acquisition conducted in this way produced data with  
Figure 14: Power Spectral Density (PSD) for artifact-free EEG signals. (**a**) Climatic chamber. (**b**) In pasture. Figure 15: STFT for the second artifact-free EEG signal segment in Figure 13b. a greater number of artifacts, but it had the advantage of reducing the discomfort caused by using subdermal electrodes. Another important point to consider is the challenge imposed by the size of the animals. Considering the larger amount of data, this methodology could be applied to investigate changes in brain electrical activity during animal farming, to monitor brain pathologies, and to other situations. Table 1 lists the normalized complexity for seven known sequences. Table 2 contains the C(N) for the artifact-free signal sections in Figure 13.  
We obtained values similar to the values in Table 2 for the other five animals we evaluated. High normalized LZC C(N) values can be associated with the presence of noise, random numbers, or the predominance of one of these events (Table 1). On the basis of the values we observed for bovine signals (Table 2), we can consider that the artifacts were not completely eliminated and that cattle brain signals have some random characteristics.