Title :
Phase space analysis of Event Related Potential during episodic memory retrieval
Author :
Ghoshuni, M. ; Nasrabadi, A.M. ; Khalilzadeh, M.A. ; Hashemibd, M.R.
Author_Institution :
Islamic Azad Univ., Mashhad
Abstract :
In this study we have used recurrence analysis (RA) to classify ERPs that appear from episodic memory retrieval in old/new recognition task. Since RA is based on embedding phase space, we have used correlation dimension and autocorrelation function in order to estimate embedding dimension and the lag time between successive components of each of embedding space vectors alternatively. According to RA the rates of classification have been improved in comparison to pervious study. We have obtained 98.9% accuracy for train data and 97.7% for test data. Furthermore we could classify ERPs with line interference noise using RA.
Keywords :
bioelectric potentials; brain; correlation methods; medical signal processing; phase space methods; signal classification; autocorrelation function; correlation dimension; embedding dimension; episodic memory retrieval; event related potential; lag time; line interference noise; phase space analysis; recurrence analysis; signal classification; Biomedical engineering; Conducting materials; Data analysis; Electrodes; Electroencephalography; Enterprise resource planning; Fractals; Interference; Signal analysis; Signal generators; Algorithms; Data Interpretation, Statistical; Electroencephalography; Equipment Design; Evoked Potentials; Humans; Male; Memory; Mental Processes; Models, Statistical; Models, Theoretical; Reaction Time; Reproducibility of Results; Signal Processing, Computer-Assisted;
Conference_Titel :
Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE
Conference_Location :
Lyon
Print_ISBN :
978-1-4244-0787-3
DOI :
10.1109/IEMBS.2007.4352710
