Volume 6, Issue 2 (3-2010)                   JSDP 2010, 6(2): 39-52 | Back to browse issues page

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Applying Recurrence Plots for Identifying Memory Components in Single-Trial EEGs
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Abstract: The purpose of this study was to apply recurrence plots on event related potentials (ERPs) recorded during memory recognition tests. EEG signals recorded during memory retrieval in four scalp region were used. Two 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 complexity and back to its basic state. The mean amplitude of Old items was more than New one. Furthermore we applied statistical analysis (t-test) to find meaningful difference between features extracted from nonlinear measures. Using this method, we found its ability to detect memory components of EEG signals and to 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.

Keywords: memory, event related potentials (ERP), recurrence quantification analysis, nonlinear analysis, single trial analysis.
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Type of Study: Research | Subject: Paper
Received: 2010/03/20 | Accepted: 2018/02/21 | Published: 2018/02/21 | ePublished: 2018/02/21
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