**Abstract**

The exponential growth of wired and wireless technologies which generate Electromagnetic Interference (EMI) has made obtaining microvolt-level bioelectric signals challenging. While digital filtering algorithms provide a wealth of information and allow Artificial Intelligence (AI) to interpret the data, the process may denigrate the integrity of the original signal. Busy clinicians and researchers have relied on computer-analyzed ECG, losing their ability to discriminate between data of high quality and data contaminated with EMI (noise). Resolving an EMI issue with a microphone is one way to learn the methodology. A step-by-step process of troubleshooting EMI in an audio application provides a framework for understanding the fundamental variables that generate EMI and a better understanding of analog electronics. The troubleshooting methodology applies to resolving EMI issues with all biologic signals including Surface Electromyography (SEMG), EEG, ECG, and Needle EMG. As we enter the age of extended range WIFI and cellular technologies, understanding analog electronics is crucial in ensuring we obtain clean data for more clinically meaningful results.

**Keywords:** electromagnetic interference, EMI, EMF, surface EMG, SEMG, electrical interference, shielding, EMF reduction, digital signal processing, filtering EMI, microphone, USB-powered microphone
