Before we are going to add the blanking plugin let's just see what it is and discuss the good and the bad things.
Generally speaking, blanking is a simple way to deal with stimulation artifacts by simply ignoring them. Originally, blanking is a way to protect the recording device from high-amplitude stimulation pulses. Here, blanking means that the recording is turned off during the stimulation and turned on after the stimulation is finished. This procedure protects the recording device -- the amplifier -- from going into saturation.
However, in our case, we do record the stimulation pulses and perform the blanking in software by replacing the stimulation pulses with a linearly interpolated segment.
Blanking is a very simple way of dealing with artifacts. Unfortunately, in many cases -- basically, every time multiple stimulation pulses are used -- valuable neural responses are removed and can not be analyzed anymore. Therefore, a better but also more complicated and computationally more expensive way to deal with artifacts is to use prior knowledge about the signal to remove the artifacts and therefore reconstruct the original uncontaminated part of the signal.
Furthermore, another aspect of blanking is the possibility of inadvertently removing valuable neural responses which then may negatively affect the spike train statistics. This problem has been discussed in [Joseph K, Mottaghi S, Christ O, et al. 2018]
[Joseph K, Mottaghi S, Christ O, et al. 2018] Joseph K, Mottaghi S, Christ O, Feuerstein TJ, Hofmann UG. When the Ostrich-Algorithm Fails: Blanking Method Affects Spike Train Statistics. Front Neurosci. 2018 Apr 30;12:293. doi: 10.3389/fnins.2018.00293. PMID: 29780301; PMCID: PMC5946007.