||Electromyography (EMG) is the record of the electrical activity from muscle fiber membranes. This invaluable clinical tool in neurology aids in the diagnosis and monitoring of disease affecting muscle and nerve. Routine clinical EMG studies rely on the experience of the physician to analyze the data in a qualitative manner. Quantitative EMG (QEMG) refers to a number of techniques that measure various aspects of the EMG signal and result in statistical data. These techniques are becoming broader in scope, more automated and increasingly available on EMG machines. However, QEMG studies have challenges in a number of operational parameters from the engineering perspective but may not accurately fit from the physiologic and pathologic perspectives, and a number of these issues have not been investigated in a systematic way. Here we present a number of studies aimed at validating and improving clinical usability of QEMG. First, we compare three QEMG algorithms available on EMG machines for use in the clinic, a study not performed previously. We determined that two algorithms yield similar results with minimal user intervention, while the third requires considerable expert review of the results and which are less robust than with the first two. Second, we show that among available sizes of intramuscular needle electrodes the smaller diameter electrode yields data comparable to the larger diameter electrode for clinical QEMG. Third, we show that any needle electrode position along the longitudinal axis of the muscle with respect to the distribution of neuromuscular junctions within the muscle is acceptable for clinical QEMG studies. Fourth, we investigate and find that high-pass filtering is not an effective means of extracting more sensitive information from the EMG signals. Finally, we determine that at each position of the electrode within the muscle, 10 s worth of data collection balances the need to collect sufficient data with the possibility of degrading the signal due to subtle physiologic movements. The results of these efforts are a better understanding of the practical limits of the QEMG algorithms and how operational parameters can be optimized for more accurate statistics, more rapid data acquisition, and greater patient tolerability.