Systems and method for conduction of single trial classification of EEG signals of a human subject generated responsive to a series of images containing target images and non-target images, the method comprising: obtaining said EEG signals in a spatio-temporal representation comprising time points and respective spatial distribution of said EEG signals; classifying said time points independently, using a linear discriminant classifier, to compute spatio-temporal discriminating weights; using said spatio-temporal discriminating weights to amplify said spatio-temporal representation by said spatio-temporal discriminating weights at tempo-spatial points respectively, to create a spatially-weighted representation; using Principal Component Analysis (PCA) on a temporal domain for dimensionality reduction, separately for each spatial channel of said EEG signals, to create a PCA projection; applying said PCA projection to said spatially-weighted representation onto a first plurality of principal components, to create a temporally approximated spatially weighted representation containing for each spatial channel, PCA coefficients for said plurality of principal temporal projections; and classifying said temporally approximated spatially weighted representation, over said number of channels, using said linear discriminant classifier, to yield a binary decisions series indicative of each image of the images series as either belonging to said target image or to said non-target image.
CLASSIFYING EEG SIGNALS IN RESPONSE TO VISUAL STIMULUS