What issue can arise from undersampling in digital instrumentation?

Undersampling in digital instrumentation can lead to aliasing, which is a phenomenon where higher frequency signals are misrepresented as lower frequency signals due to insufficient sampling rates. According to the Nyquist-Shannon sampling theorem, to accurately capture a signal without distortion, it must be sampled at least twice its highest frequency component. If the sampling rate falls below this threshold, the higher frequency components can fold back into the lower frequency range, creating misleading representations of the signal.

Aliasing can severely impair the quality of the data collected, making it difficult to accurately analyze or interpret the signals. This is especially critical in applications like EEG monitoring, where misrepresentation of brain wave frequencies could lead to incorrect diagnoses or treatment decisions.

In contrast, common mode rejection entails the ability of a measurement system to eliminate noise that is common to both the positive and negative inputs, and impedance relates to the opposition that a circuit presents to a current when a voltage is applied. The fast Fourier transform is a mathematical algorithm used to compute the discrete Fourier transform rapidly, primarily for the analysis and interpretation of sampled signals, but it does not inherently connect with the issue of sampling rates.