In EEG signals, common noise signals are:

In the context of EEG signals, the correct choice highlights that common noise signals are often recognized and effectively removed from the recordings. This is a critical step because EEG data can be significantly impacted by various forms of noise, including electrical interference and artifacts from muscle activity or eye movements. The process of elimination involves using techniques like filters and signal processing algorithms designed to isolate the actual brain activity from these extraneous signals.

While the goal is to provide the clearest representation of brain activity, it is not sufficient to merely minimize or amplify noise. Minimizing can reduce the effects of noise but may not fully remove it. Amplifying, on the other hand, can increase the visibility of both brain signals and noise, which is counterproductive. Therefore, effective elimination is essential for accurate interpretation of EEG data, making this understanding crucial for anyone working within the field of electroencephalography.