Identifying informative features for ERP speller systems based on RSVP paradigm

This preliminary study focused on identifying informative features in the frequency and spatial domains for single-trial Event Related Potential (ERP) detection for ERP spelling systems. A predefined sequence of letters was presented to subjects in a Rapid Serial Visual Presentation (RSVP) paradigm. EEG data were collected and analyzed offline. A Linear Discriminant Analysis (LDA) classifier was selected as ERP detector for its simplicity and robustness. A range of features in different frequency bands and EEG channel subsets was extracted and detection accuracies were compared for different classes of features.