  Formulating mitigation strategies is one of the main aspect in the dynamical
study of extreme events. Apart from the effective control, easy implementation
of the devised tool should also be given importance. In this work, we analyze
the mitigation of extreme events in a coupled FitzHugh-Nagumo (FHN) neuron
model utilizing an easily implementable constant bias analogous to a constant
DC stimulant. We report the route through which the extreme events gets
mitigated in $Two$, $Three$ and $N-$coupled FHN systems. In all the three
cases, extreme events in the observable $\bar{x}$ gets suppressed. We confirm
our results with the probability distribution function of peaks, $d_{max}$ plot
and probability plots. Here $d_{max}$ is a measure of number of standard
deviations that crosses the average amplitude corresponding to $\bar{x}_{max}$.
Interestingly, we found that constant bias suppresses the extreme events
without changing the collective frequency of the system.
