A Deep Learning method for classification of images RSVP events with EEG data

In this paper, we investigated Deep Learning (DL) for characterizing and detecting target images in an image rapid serial visual presentation (RSVP) task based on EEG data. We exploited DL technique with input feature clusters to handle high dimensional features related to time - frequency events. The method was applied to EEG recordings of a RSVP experiment with multiple sessions and subjects. For classification of target and non-target images, a deep belief net (DBN) classifier was based on the uncorrelated features, which was constructed from original correlated features using clustering method. The performance of the proposed DBN was tested for different combinations of hidden units and hidden layers on multiple subjects. The results of DBN were compared with cluster Linear Discriminant Analysis (cLDA) and Support vector machine (SVM) and DBN demonstrated better performance in all tested cases. There was an improvement of 10 - 25% for certain cases. We also demonstrated how DBN is used to characterize brain activities.