Recurrence plots for identifying memory components in single-trial EEGs

The purpose of this study was to apply recurrence plots and recurrence quantification analysis (RQA) on event related potentials (ERPs) recorded during memory recognition tests. Data recorded during memory retrieval in four scalp region was used. Tow most important ERP's components corresponding to memory retrieval, FN400 and LPC, were detected in recurrence plots computed for single-trial EEGs. In addition, the RQA was used to quantify changes in signal dynamic structure during memory retrieval, and measures of complexity as RQA variables were computed. Given the stimulus, amplitude of the RQA variables increases around 400ms, corresponding to dimension reduction of system. Furthermore, after 800ms these amplitudes decreased which can be as a consequence of an increase in system dimension and back to its basic state. The mean amplitude of Old items was more than New one. Using this method, we found its ability to detect memory components of EEG signals and do a distinction between Old/ New items. In contrast with linear techniques recurrence plots and RQA do not need large number of recorded trials, and they can indicate changes in even single-trial EEGs. RQA can also show differences between old and new events in a memory process.