## 4 Discussion
### Future Perspectives and Limitations  
Since human sEMG signal processing standards emphasize the importance of optimizing cut-off frequencies to strike a balance between noise reduction and signal preservation [16; 34], further research should focus on determining the most appropriate filtering parameters for each equine muscle, gait, and specific noise sources, as well as on setting an acceptable threshold for signal loss. Moreover, in humans, the amplitude of sEMG signals shows a relatively linear decrease with increasing cut-off frequency [16]. However, no similar evidence has been published for horses. Therefore, further equine studies should also be conducted to confirm or refute a linear relationship between amplitude [19; 20; 22] and amplitude-dependent signal features--such as RMS [5], integrated electromyography (iEMG) [6], amplitude envelope, which is referred to as an average rectified value (ARV) [6; 21], and SNR [4]--and cut-off frequencies, following consistent and gradual application of high-pass and low-pass cut-off filtering.  
However, it should be emphasized that the generalizability of the discussed results is limited due to the small sample size used in this preliminary study. One may observe that, due to the complexity and challenges associated with performing sEMG recording in horses, study groups are often small and heterogeneous. For example, the study on the optimal placement of electrodes to measure equine myoelectric activity [4] was conducted on only three horses. Similarly, the investigation into the effects of trotting speed on muscle activity and kinematics included four horses ridden under saddle from two different breeds [35], and five horses of two breeds on a treadmill [36]. Other studies have included six [37], seven [15; 38], or more horses [5; 12; 19; 20; 21; 22], with some supporting their sample size through reliable power analysis. It is important to emphasize that this study is a preliminary study, aimed at generating empirical data necessary for accurate sample size calculation prior to more extensive methodological investigations on sEMG signal filtering. Therefore, while the number of horses examined in the present study is small, it aligns with previous preliminary work such as that by [PERSON] et al. [4] and can be considered sufficient for obtaining initial findings. Interestingly, no previous equine sEMG studies have attempted to investigate breed-dependent differences, even when eight or more horses from two or more breeds were included [5; 19; 20; 21; 22]. In this study, only three horses representing one breed were examined, limiting breed-related conclusions. Since breed differences are an interesting aspect of equine sEMG signal characteristics, this research direction can be considered as a promising future perspective. As in all previous equine sEMG studies, a limitation of this work stems also from the manual annotation of burst and noise segments, which introduces potential bias due to the subjective nature of ROI selection. In this research, the annotation protocol used by [PERSON] et al. [4] was followed, as it accounted for both burst and noise segments, which was essential for calculating the SNR. While this risk could be reduced by using an automatic burst activity detector, no such tool has yet been developed specifically for equine sEMG signals. Developing such a tool represents an important future research direction that could significantly advance the field of equine sEMG studies.