Classification of Cortical Responses Using Features from Single EEG Records

Our research goal is to develop a new methodology for studying brain function using single, unaveraged EEG records. This investigation has led to a new algorithm for feature extraction for the case of small design (learning) sets. The algorithm has been applied to extract features from unaveraged (single) EEG records, which consist of single evoked responses elicited from human subjects who read textual material presented in the form of propositions. The subjects were instructed to make a binary decision concerning each proposition. This gave two possible data classes. We selected features from the evoked event-related potentials (ERP's), and designed a classifier to assign the ERP's for each proposition to one of the two possible classes.