Aliasing in EEG results from what issue?

Aliasing in EEG occurs primarily from an insufficient sampling rate. This phenomenon happens when the signal being recorded is sampled at a rate that is too low to accurately capture the frequency content of that signal. According to the Nyquist theorem, to accurately reconstruct a signal, it must be sampled at least twice its highest frequency. If this condition is not met, higher frequencies can be misrepresented as lower frequencies, leading to distortion in the recorded data, which is termed aliasing.

For example, if an EEG system is only sampling at a low rate, it may fail to capture rapid fluctuations in brain waves that occur at higher frequencies, thus misrepresenting them within the EEG recording. This can result in an unintentional overlap of higher frequency signals that are incorrectly interpreted as lower frequencies, compromising the integrity of the EEG data collected.

In contrast, excessive sampling rates do not lead to aliasing but rather can produce large data sets that may be more challenging to analyze without necessarily affecting the data quality. Inaccurate channel calibration can affect the amplitude of the recording but is not directly related to aliasing. Electrical interference can introduce noise into the signal but does not cause the misrepresentation of frequencies inherent to the definition of aliasing.