EEG based brain state decoding has numerous applications. State of the art
decoding is based on processing of the multivariate sensor space signal,
however evidence is mounting that EEG source reconstruction can assist
decoding. EEG source imaging leads to high-dimensional representations and
rather strong a priori information must be invoked. Recent work by Edelman et
al. (2016) has demonstrated that introduction of a spatially focal source space
representation can improve decoding of motor imagery. In this work we explore
the generality of Edelman et al. hypothesis by considering decoding of face
recognition. This task concerns the differentiation of brain responses to
images of faces and scrambled faces and poses a rather difficult decoding
problem at the single trial level. We implement the pipeline using spatially
focused features and show that this approach is challenged and source imaging
does not lead to an improved decoding. We design a distributed pipeline in
which the classifier has access to brain wide features which in turn does lead
to a 15% reduction in the error rate using source space features. Hence, our
work presents supporting evidence for the hypothesis that source imaging
improves decoding
