What are aliasing errors? Are they hard to detect?
An alias is a false lower frequency component that appears in sampled data acquired at too low a sampling rate. Aliasing errors occur when components of a signal are above the Nyquist frequency (Nyquist theory states that the sampling frequency must be at least two times the highest frequency component of the signal) or one half the sample rate. For example, if you are acquiring data from eight channels at 100k samples/second, the sampling rate for one channel is 100 ksamples/second * 8, or 12.5 ksamples/second. In this case, any signal component with a frequency above 6.25 kHz will cause aliasing errors. Aliasing errors are hard to detect and almost impossible to remove using software. The solution is to use a high enough sampling rate, or if this is not possible, to use an anti-aliasing filter in front of the analog-to-digital converter (ADC) to eliminate the high frequency components before they get into the data acquisition system.
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