## 1 Introduction  
Many biological systems emit signals. If these signals are interpreted correctly, they can provide information about the processes that occur in the system, adding to knowledge about biological systems. Electroencephalography (EEG) is the most common method to access brain information. During EEG, brain electrical activity is recorded by placing electrodes on the scalp. This method has high temporal resolution and is safe, easy to use,and affordable [1; 2]. In the last few decades, great effort has been devoted to understanding the human brain. This effort has allowed neurological disorders such as epilepsy [3; 4; 5] and Parkinson's disease [6; 7; 8] to be diagnosed through the brain-computer interface [6; 9; 10]. Several DSP techniques have already been applied to humans and laboratory animals, e.g., FFT (Fast Fourier Transform) [11], wavelets [12], AGR (Adaptive Gaussian Representation) [13], ANN (Artificial Neural Network) [14; 15], Lempel-Ziv complexity [16], and entropy [17], among others [18; 19]. This kind of technology has also been employed to investigate some diseases in domestic pets [20; 21; 22]. Concerning farmed animals, most of the effort that has been put in understanding brain processes has focused on pre-slaughter sensitization [23; 24]. The exception is a recent study on sheep [25], which involved a method that did not use restraint and employed implanted electrodes to analyze animal brain signals instead. Within the Brazilian agribusiness, meat production is one of the items whose contribution to the trade balance has grown the most, thereby making bovines an important field of study [26]. In this scenario, understanding the brain processes in bovines will allow some situations such as heat stress [27; 28] and pain [29; 30], among others [24], to be controlled. Acquiring brain signals in animals that can move freely presents various challenges, and the number of research studies dealing with bovine brain electrical activity is limited. Most of these studies have been conducted in laboratory conditions [31] and used subdermal electrodes [31; 32] in animals that were subjected to some type of containment [24; 32]. These conditions are unrealistic when we consider farmed animals, mainly large ones [25]. Telemetric recording systems can monitor physiological parameters in freely moving animals without any restrictions in their exploratory behavior [33]. In almost five decades, different systems have been developed, from large systems [34] to transmitters that can be implanted beneath the skin of small animals (e.g., rats) [35]. Another challenge associated with electroencephalography involves the presence of artifacts within the signal. Given the nature of the acquisition process, the EEG signal can mix with other biological potentials. Broadly speaking, artifacts can originate from internal (physiological activities of the subject) and external sources (environmental interference, equipment, electrode pop-ups, and cable movement) and interfere with recordings in both temporal and spectral domains [2]. Where signals from animals are concerned, the presence of artifacts is hardly avoided. In the case of bovines specifically, there is the additional challenge of prolonged rumination throughout the day. Given the number of research studies on bovine brain signal analysis and the kind of signal processing techniques they apply [23; 24; 31; 32; 34; 36; 37; 38], a good strategy to characterize these signals is to assume that they are nonstationary, as in humans, and to employ time-frequency techniques to estimate the spectra. EEG is typically described in terms of rhythms and transients. The rhythmic activity of EEG is divided into frequency bands [1; 2]. This characteristic makes traditional analysis rely mainly on the detection of spectral power changes [39]. The Power Spectral Density (PSD) is calculated by Fourier Transform, the estimated autocorrelation sequence that is found by nonparametric methods. One of these methods is [PERSON]'s method [40].