# A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression
## 3 Materials and Methods: A Novel Proposed EEG Analog Front-End Circuit System  
With respect to the goal of this study, to the best of our knowledge in recent bibliography, there is a lack of EEG circuits that can combine concurrently, on the one hand, the monitoring of the theta, alpha, and beta brain wave frequency bands during usage time in which the user performs strong compound muscle movements (that is, the EEG usage protocol or protocol named for simplicity in this study), and on the other hand, employing an EEG circuit that is mobile, low-cost, and with sufficient muscle artifact handling. In this study, the design of a novel EEG Analog Front-End Circuit System is proposed that can handle the problem; the aforementioned protocols containing compound muscle movements generate substantial muscular artifacts in the raw EEG data [24]. The electronic equipment requirements of this kind of protocol can be satisfied by the proposed system in this study, which is battery-supplied in order to cover this mobility demand, uses simple and minimal sub-circuits to retain low cost (complex techniques such as chopping are avoided) and employs the necessary filters before and after the amplification stage to handle muscle artifacts sufficiently. The protocols for which the proposed system are designed could apply in neurofeedback training and tele-controlling devices using Brain-Computer Interfaces (BCIs) in which the EEG user performs muscle compound movements [3; 4]. Additionally, before implementing the simulation test, in this study, a realistic electronic model of the human brain and body is created that considers the factor of EMI from the external environment, in order to produce an accurate and mainly reproducible input signal to avoid the phenomenon of 'Garbage In, Garbage Out' (GIGO) during the evaluation of a system. In contemporary engineering terminology, as presented in [51; 52], GIGO is a concept that suggests that the quality of output in a system is determined by the quality of input. Incorrect input, such as incorrect data, can lead to incorrect output. GIGO is commonly used in mathematics and computer science but can also be applied to decision-making systems where precise data are crucial for accurate results. Figure 5 serves as a comprehensive overview of the circuit, providing a visual high-level reference that nonetheless includes all the elements and sub-circuits of the EEG circuit. Figure 4: Double notch filter circuit design of EEG. The aforementioned proposed EEG circuit design's requirements are presented. Firstly, a Common Mode Rejection Ratio (CMRR) greater than 90 dB is explained in [53]. Furthermore, the noise within the frequency range of 0.1 to 10 Hz is required to remain below approximately 500 nVpp root mean square noise [28]. The bandwidth of the input signal for this novel EEG is required to be denoised accurately, and it is from 0.5 Hz [53] to 3000 Hz in order to include a wide range of brain activity [54], with a specialized focus on the 4 Hz to 30 Hz frequency region. This special range of frequency is essential for monitoring the theta, alpha, and beta brain wave bands. Additionally, the circuit design demands a careful choice of a high input resistance, traversed by a maximum current of less than 10 \(\upmu\)V, for human-user safety reasons. This complies with safety standards for medical or research environments. The selection of component values is made with consideration for future design iterations since the printed circuit board (PCB) layouts need capacitors with a maximum value of 100 nF [46]. Prior to conventional designs, the proposed circuit specifically tackles notable issues; including inadequate filtering, limited bandwidth, and additional noise introduction prior to the amplification phase [47; 48; 49]. Our cost-effective approach aims to encourage research and commercial use while maintaining the high-quality signal integrity required in medical-grade applications.