## 3 Results  
The evaluation of the proposed system is performed by implementing the right-leg driving configuration reported in the literature [44; 47; 50] and the proposed controller on a population of twenty individuals (twenty measurements per configuration). For the acquisition of biopotentials, no digital filtering was applied in MATLAB to the signals obtained with the systems used. Figure 7 also presents the methodology used for the measurement and acquisition of EEG, sEMG, and ECG biopotentials, with electrode connections represented by a solid line  
Figure 8: Methodology used to calculate the SNR, obtaining the values of \(V_{signal}\) and \(V_{noise}\). Figure 9: Configuration used to measure the CMRR of the system [37]. (\(\beta\) signal in the parietal and frontal regions, \(\alpha\) signal in the parietal and occipital regions), a dashed line (signal in the biceps and brachioradialis muscles), and a dotted line (signal from the heart, with electrodes placed between the fourth intercostal space at the right border of the sternum, the fifth intercostal space along the mid-clavicular line, and at the level of the fifth intercostal space on the left anterior axillary line), respectively. The electrode placement is consistent across both the proposed configuration and the configuration reported in the literature for the different types of biopotentials. The Wilcoxon method [56; 57; 58; 59; 60; 65] is used to determine if there are significant differences between the signals acquired from the two configurations, while the Bland-Altman method [56; 57; 58; 59; 60; 65] compares the signals obtained from both configurations to assess the feasibility of using the proposed configuration. Table 1 presents the statistical results of both methods, showing that, according to the Wilcoxon method, the distributions of the two samples are similar. A significance level of \(\alpha\) = 0.05 (indicated in Table 1) and a 95% confidence interval were used. The average value and standard deviation were calculated to compare the systems. Ideally, the difference in their means should tend to zero; a difference of 0.04 was obtained. To verify system viability, we applied the Wilcoxon method, where the null hypothesis was accepted, indicating no significant difference between the samples, as the \(p\)-value obtained was greater than the significance level. This suggests that the samples obtained from the tested system agree with those from the proposed system. In the Bland-Altman method, as shown in Figure 10, a 95% confidence interval (CI) was used, and the difference in means tended toward zero, indicating that the proposed configuration yields similar results to those reported in the literature. Figure 11 presents the experimental results of the CMRR of the proposed system. It is observed that the system achieves a CMRR of 141.767 dB when a peak-to-peak voltage of 180 Vpp is applied, corresponding to an RMS voltage of 127 volts at 60 Hz. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**System** & **Average (\(\mu\))** & **Standard Deviation (\(\sigma\))** & \(p\)**-Value Wilcoxon/\(\alpha\)** \\ \hline Literature & 0.158 & 0.088 & 0.546/0.05 \\ Proposal & 0.195 & 0.106 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistic values obtained from two systems. Figure 10: Bland–Altman plot. The CMRR expresses the ability to suppress common-mode signals, which are very large in the human body; therefore, for the overall circuit, the RLD circuit is adopted to minimize the common-mode interference. For this reason, the importance of this work is a new configuration using an Active Shield to improve the noise reduction. Also, a comparative performance, as a function of the CMRR with different state-of-the-art biopotential readout circuits, is presented [66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77]. Table 2 summarizes the performance comparisons, where it is seen that the proposed system provides the better CMRR result in comparison with other state-of-the-art systems.