Suitable metrics for summarizing or visualizing the spatial activation of components after temporal Independent Component Analysis (ICA)
As we know from the instantaneous mixture model of ICA, a signal mixture $X_{N,T}$ is composed of an unknown mixing matrix $D_{N,N}$, and an unknown signal matrix $S_{N,T}$, where:

$$
X = DS
$$

In ICA, we solve for $D_{N,N}$ and $S_{N,T}$ via maximization of absolute kurtosis. (As one method).

If we now look at the $i$th column of $D$, each element therein will now give us weight by which the $i$th signal gets weighted by in all $N$ mixtures. That is, each element $D_{n,i}$, (row $n$, column $i$), gives us signal $i$'s contribution to mixture $n$.  



Those are called 'spatial weights' simply because in many ICA applications, we are de-mixing signals that sample a spatial field at different locations. Thus, each element of a column $D$ corresponds to a weight in space, that weighs signal source $i$. 

In this way, we can show those weights in space as the heatmap above shows, and find the weight each signal gets weighted by, for a particular mixture, at different locations in space.