CLASSIFYING EEG SIGNALS IN RESPONSE TO VISUAL STIMULUS

Systems and method for classifying EEG signals of a human subject generated responsive to a series of images containing target images and non-target images. The EEG signals are in a spatio-temporal representation. The time points are classified independently, using a linear discriminant classifier, to compute spatio-temporal discriminating weights that are used to amplify the spatio-temporal representation, to create a spatially-weighted representation. Principal Component Analysis is used on a temporal domain for dimensionality reduction, separately for each spatial channel of the signals, to create a projection, which is applied to the spatially-weighted representation onto a first plurality of principal components, to create a temporally approximated spatially weighted representation. The temporally approximated spatially weighted representation is classified over the 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.