## 2 Materials and Methods  
Some works related to biopotential acquisition are presented as follows: (1) The development of a BCI interface for acquiring steady-state visual evoked potentials using the ADS1298 IC is shown in [45]. This BCI uses the Fast Fourier Transform (FFT) for data processing. Its disadvantages include a fixed gain, a lack of modularity, and the inability to adjust the analog-to-digital converter (ADC) resolution. Additionally, it does not specify a configuration for shielding control to reduce common-mode voltage. (2) Based on an MSP430G2553 with a 10-bit resolution ADC, a BCI was developed [46]. This system includes signal pre-amplification using OPA333 operational amplifiers with gains of 1001 and 8.2. Its disadvantages are a lack of modular implementation, and the use of an existing shielding control circuit reported in the literature for reducing common-mode voltages. (3) In [47], an acquisition card based on an ADuC841 with a 12-bit ADC, which increases the computational load of the system, is presented. This BCI incorporates preamplification stages of 1000 and 3300 using a network of TLV2264 and OPA4344 operational amplifiers, respectively. Disadvantages include the absence of a modular architecture to increase the number of acquisition channels and the use of a shielding driver circuit, based on the literature-reported configurations with operational amplifiers, to reduce common-mode voltage. (4) A BCI is implemented using a closed system called Emotiv Epoc [48], which performs the acquisition and analysis of visual evoked potentials with classification algorithms. As a closed system, it has the disadvantage of not allowing electrode reduction or software modifications, limiting potential improvements. (5) Finally, a BCI is implemented using an INA118U instrumentation amplifier and an MC33171 DG operational amplifier for common-mode shielding and voltage reduction control. However, it uses a 24-bit ADS1252 ADC, which increases computational load [49].  
Biopotential acquisition systems present several drawbacks, highlighting the need for improvements in the following areas: (1) Electronics for the acquisition of different types of biopotentials (EEG, ECG, and sEMG) that could be adapted to a specific application or study, with a modular configuration that could allow additional modules to expand the number of electrodes for signal acquisition. This benefits end users in analyses or applications requiring multiple types of biopotentials, such as single-trial EEG-sEMG coherence analysis to identify progressive muscle fatigue-related alterations in corticomuscular coupling, multimodal fusion of muscle and brain signals for hybrid BCI generation, fused ECG-sEMG signals for physiological rehabilitation devices, fused EEG-ECG signals for epilepsy diagnosis, or prosthetic designs combining EEG and sEMG signals. (2) Scalability with adjustable gain over a wide range, selectable by hardware, as well as ADC resolution, can be changed if desired by the user, either to reduce computational load or to test with different dedicated cards. (3) The enhanced reduction of a 60 Hz noise introduced by the electrical grid, which interferes with acquired biopotentials and produces a louder, higher-amplitude signal that impacts subsequent analysis. Solutions to this issue typically involve using a shielding controller with operational amplifiers to reduce common-mode voltage between the electrode terminals and the system input.