APPLYING RECURRENCE PLOTS FOR IDENTIFYING MEMORY COMPONENTS IN SINGLE-TRIAL EEGS

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

TALEBI NASIBEH; M. NASRABADI ALI

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: SIGNAL AND DATA PROCESSING Year:2009 | Volume:- | Issue:2 (SERIAL 12) Page(s): 39-52

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

1,369

Download:

Cites:

Information Journal Paper

Title

APPLYING RECURRENCE PLOTS FOR IDENTIFYING MEMORY COMPONENTS IN SINGLE-TRIAL EEGS

Author(s)

TALEBI NASIBEH | M. NASRABADI ALI | Issue Writer Certificate

Keywords

MEMORYQ2

EVENT RELATED POTENTIALS (ERP)Q3

RECURRENCE QUANTIFICATION ANALYSISQ3

NONLINEAR ANALYSISQ3

SINGLE TRIAL ANALYSISQ3

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.

Cites

No record.

References

No record.

Cite

APA: Copy

TALEBI, NASIBEH, & M. NASRABADI, ALI. (2009). APPLYING RECURRENCE PLOTS FOR IDENTIFYING MEMORY COMPONENTS IN SINGLE-TRIAL EEGS. SIGNAL AND DATA PROCESSING, -(2 (SERIAL 12)), 39-52. SID. https://sid.ir/paper/160806/en

Vancouver: Copy

TALEBI NASIBEH, M. NASRABADI ALI. APPLYING RECURRENCE PLOTS FOR IDENTIFYING MEMORY COMPONENTS IN SINGLE-TRIAL EEGS. SIGNAL AND DATA PROCESSING[Internet]. 2009;-(2 (SERIAL 12)):39-52. Available from: https://sid.ir/paper/160806/en

IEEE: Copy

NASIBEH TALEBI, and ALI M. NASRABADI, “APPLYING RECURRENCE PLOTS FOR IDENTIFYING MEMORY COMPONENTS IN SINGLE-TRIAL EEGS,” SIGNAL AND DATA PROCESSING, vol. -, no. 2 (SERIAL 12), pp. 39–52, 2009, [Online]. Available: https://sid.ir/paper/160806/en

Related Journal Papers