Mohamed Saaid, M.F. and Wan Abas, Wan Abu Bakar and Arof, Hamzah and Mokhtar, N. and Ramli, R. and Ibrahim, Z. (2011) Change point detection of EEG signals based on particle swarm optimization. In: 5th Kuala Lumpur International Conference on Biomedical Engineering, BIOMED 2011, Held in Conjunction with the 8th Asian Pacific Conference on Medical and Biological Engineering, APCMBE 2011, 2011, Kuala Lumpur.

PDF (Change point detection of EEG signals based on particle swarm optimizatio)
Change_Point_Detection_of_EEG_Signals_Based_on_Particle_Swarm_Optimization.pdf  Other Download (206kB) 
Abstract
This paper proposes a change point detection for electroencephalograms (EEG) signal application based on Particle Swarm Optimization (PSO). As EEG signal is well known consider as nonstationary in nature, we model the signal by using the sinusoidalHeaviside function, which are capable to represent the change of the behavior of the signal. The parameter of the model with the change point location can be tuned by finding the minimum value of sum squared error. It was showed that the minimum value of sum squared error in the parameter tuning give the exact location of change point. The proposed method is applied to the human EEG during an eye moving task.
Item Type:  Conference or Workshop Item (Paper) 

Funders:  UNSPECIFIED 
Additional Information:  Conference code: 85436 Export Date: 24 February 2014 Source: Scopus Language of Original Document: English Correspondence Address: Mohamed Saaid, M. F.; Department of Biomedical Engineering, Faculty of Enginnering, University of Malaya, Kuala Lumpur, Malaysia; email: mfms@um.edu.my References: Smith, J., Jones Jr., M., Houghton, L., Putare of health insurance (1999) N Engl J Med, 965, pp. 325329; Tseng, S.Y., Chen, R.C., Chong, F.C., Kuo, T.S., Evaluation of parametric methods in EEG signal analysis (1995) Med Eng Phys, 17 (1), pp. 7178; Debener, S., Ullsperger, M., Siegel, M., Engel, A.K., Singletrial EEGfMRI reveals the dynamics of cognitive function (2006) Trends in Cognitive Sciences, 10 (12), pp. 558563. , DOI 10.1016/j.tics.2006.09.010, PII S1364661306002725; Subasi, A., Automatic detection of epileptic seizure using dynamic fuzzy neural networks (2006) Expert Syst. Appl., 31, pp. 320328; Shin, H.C., Jia, X., Nickl, R., Geocadin, R.G., Thakor, N.V., A subbandbased information measure of EEG during brain injury and recovery after cardiac arrest (2008) IEEE Trans. Biomed. Eng., 55 (8), pp. 19851990; Petit, D., Gagnon, J.F., Fantini, M.L., FeriniStrambi, L., Montplaisir, J., Sleep and quantitative EEG in neurodegenerative disorders (2004) Journal of Psychosomatic Research, 56 (5), pp. 487496. , DOI 10.1016/j.jpsychores.2004.02.001, PII S0022399904000182; Vaughan, T.M., Wolpaw, J.R., Donchin, E., EEGbased communication: Prospects and problems (1996) IEEE Transactions on Rehabilitation Engineering, 4 (4), pp. 425430. , DOI 10.1109/86.547945, PII S1063652896093639; Basseville, M., Nikiforov, I.V., (1993) Detection of Abrupt Change: Theory and Applications, , Englewood Cliffs: Prentice Hall; Qin, D., A comparison of techniques for the prediction of epileptic seizures (1995) 8th IEEE Symp. ComputerBased Medical Systems, Lubbock, TX, 1995; Piryatinskaa, A., Terdikb, G., Woyczynskic, W.A., Loparod, K.A., Schere, M.S., Zlotnikf, A., Automated detection of neonate ERG sleep stages (2009) Computer Methods and Programs in Biomedicine, 95, pp. 3146; Adak, S., Timedependent spectral analysis of nonstationary time series (1998) J. Amer. Statist. Assoc., 93, pp. 14881501; Davis, R.A., Lee, T.C.M., RodriguezYam, G.A., Structural break estimation for nonstationary time series models (2006) Journal of the American Statistical Association, 101 (473), pp. 223239. , DOI 10.1198/016214505000000745; Ombao, H.C., Raz, J.A., Von, S.R., Malow, B.A., Automatic Statistical Analysis of Bivariate Nonstationary Time Series (2001) Journal of the American Statistical Association, 96 (454), pp. 543560. , DOI 10.1198/016214501753168244; Last, M., Robert Shumway, R., Detecting abrupt changes in a piecewise locally stationary time series (2008) Journal of Multivariate Analysis, 99 (2), pp. 191214; Kennedy, J., Eberhart, R.C., Particle Swarm Optimization (1995) Proceeding of IEEE International Conference on Neural Networks. IV. Perth, Australia, pp. 19421948. , Piscataway, N.J.: IEEE Service Center 
Uncontrolled Keywords:  change point detection, EEG, nonstationary, Particle Swarm Optimization, sinusoidal, Changepoints, EEG signals, Human EEG, Minimum value, Nonstationary, Parametertuning, Particle swarm, Sum squared error, Behavioral research, Bioelectric phenomena, Biomedical engineering, Electroencephalography, Signal detection, Particle swarm optimization (PSO). 
Subjects:  T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) 
Divisions:  Faculty of Engineering 
Depositing User:  Mr Jenal S 
Date Deposited:  10 Mar 2014 06:41 
Last Modified:  15 Nov 2019 06:02 
URI:  http://eprints.um.edu.my/id/eprint/9482 
Actions (login required)
View Item 