Large Laplacian Spatial Filter on EEG?
Laplacian

It is a 2nd derivative of the recorded voltage for each electrode. Suppose the recordings at position $(i,j)$ is $V(i,j)$, then Laplacian operation  at $(i,j)$ is 

$(V(i,j)-2V(i-1,j)+V(i-2,j))^2 + (V(i,j)-2V(i,j-1)+V(i,j-2))^2$

And it serves as a high-pass filter that enhances localized activity while suppresses the diffusion activity. The implementation of the Laplacian in EEG filtering on the voltage at each electrode is to subtract the weighted voltages from the surrounding electrodes from the voltage recording at current electrode, where the weight is electrode distance dependent. 

Large 

The difference between large and small is determined by how you select the electrodes. For the small Laplacian, you select the nearest-neighbor electrodes, while for the large Laplacian, they are the next-nearest-neighbor electrodes. See the figure below. If the signal you are interested in is less highly localized and/or varies in exact location over 
time, the large Laplacian is expected.



Read the paper Spatial filter selection for EEG-based communication by Dennis J. McFarland et all for more details.