## 2 Materials and Methods
### Signal Characterization  
For both groups of animals, all the stages of brain signal characterization were performed with the Signal Processing Toolbox(tm) from MATLAB(r) software releases R2015a and 2019a, developed by The Mathworks, Inc.  
In pre-processing stages, different methods can be employed to handle artifacts. One of them rejects the epoch or segment of EEG data that is labelled as artifactual [19]. [PERSON] et al. [62] proposed a sequential method where spectral analysis is performed after corrupted segments are extracted. For this purpose, artifacts are detected by visual inspection and then rejected. The data considered as artifacts were those that exceeded the amplifier's scale and neighboring segments. The signals used for analysis were labeled as \"artifact-free\" epochs. [PERSON]'s method was applied to artifact-free signal segments to estimate the signal PSD and to verify whether the spectral content is due to brain signals.  
PSD makes the frequency spectrum smoother than the raw FFT output, and the Welch algorithm [63] is a nonparametric method to estimate PSD. Let us consider that the FFT is calculated over the entire duration of the signal. In the Welch algorithm, instead of processing the FFT over the entire time domain, the signal is separated in windows with the same size. Window size affects the clarity of the result by cutting frequencies with periods larger than the window. Windowing is taking a sample of a larger dataset and tapering the signal at the edges of each interval. This makes the signal smoother without sharp transitions that can disturb the frequency spectrum representation [64]. [PERSON]'s method was applied to artifact-free signal segments to estimate the signal PSD.  
Considering that _STFT_ is a sequence of Fourier Transforms of a windowed signal, it can be represented as in Equation (2) [65].  
\[X_{STFT[m,n]}=\sum_{k=0}^{L-1}x[k]g[k-m]e^{-j2\pi nk/L} \tag{2}\]  
where \(x[k]\) denotes the signal, and \(g[k]\) denotes an \(L\)-point window function.  
_STFT_ was also employed to artifact-free signal segments so that data could be mapped in the time-frequency domain.  
For LZC calculation, each signal segment is converted into a binary sequence \(s(n)\), as follows [52,66]:  
\[s(n)=\begin{cases}1,\;if\;x(n)>m\\ 0,\;if\;x(n)<m\end{cases} \tag{3}\]  
where \(x(n)\) is the signal segment, and \(n\) is the segment sample index from 1 to \(N\) (segment size). The segment length \(N\) was chosen for at least 400 samples, a value for which normalized LZC is stabilized [52]; \(m\) is the signal mean value. Thereafter, the resulting binary sequence \(s(n)\) is scanned from left to right, and the number of different patterns is counted. The complexity value \(c(n)\) is increased every time a new pattern is encountered [51,52,66]. [PERSON] and [PERSON] [51] showed that  
\[\lim_{n\rightarrow\infty}c(n)=b(n)=H\frac{n}{\log_{k}n} \tag{4}\]where \(n\) is the segment length, and \(k\) is the number of different symbols in the sequence (in the binary case, \(k\) = 2, and \(H\) represents entropy (Equation (5)).  
\[H=-\frac{1}{\ln(k)}\sum_{i=1}^{k}p_{i}\text{log}(p_{i}) \tag{5}\]  
where \(p_{i}\) indicates the probability that a state \(i\) is obtained by counting the occurrences of each symbol, divided by the total number of symbols in the sequence.  
To avoid the variations due to the segment length, normalized LZC values are calculated as follows:  
\[C(N)=\frac{c(N)}{b(N)} \tag{6}\]  
The more random the sequence, the greater the number of different patterns present in it; that is, its normalized complexity \(C(N)\) approaches one. Meanwhile, \(C(N)\) values that approach zero are associated with deterministic sequences. Normalized LZV-\(C(N)\) was applied to artifact-free signal segments to investigate oscillatory, chaotic, and random components in bovine brain signals. To illustrate the relationship between \(C(N)\) and signal characteristics, low- and high-complexity known sequences were calculated.