Applications of fuzzy similarity index method in processing of hypnosis

Soroor Behbahani, Ali Motie Nasrabadi

Biomedical Engineering Department, Faculty of Engineering, Shahed University, Tehran, Iran.
Biomedical Engineering Department, Islamic Azad University, Science and Research Branch, Tehran, Iran.

DOI: 10.4236/jbise.2009.25051                

Abstract

The brain is a highly complex system. Under-standing the behavior and dynamics of billions of interconnected neurons from the brain signal requires knowledge of several signal- process-ing techniques, from the linear and non-linear domains. The analysis of EEG signals plays an important role in a wide range of applications, such as psychotropic drug research, sleep studies, seizure detection and hypnosis proc-essing. In this paper we accomplish to analyze and explore the nature of hypnosis in Right, Left, Back and Frontal hemisphere in 3 groups of hypnotizable subjects by means of Fuzzy Simi-larity Index method.

Keywords

Fuzzy Similarity Index; Hypnosis; Left- Right; Frontal-Back Hemisphere; Higuchi; Entropy; Energy; Frequency Band

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Fingelkurts, A. A., Kallio, S., and Revonsuo, A. (2007) Hypno-sis induces a changed composition of brain oscillations in EEG. Contemp. Hypnosis, 24(1), 3–18.
[2]	Xu, W., Guan, C., Siong, C. E., Ranganatha, S., Thulasidas, M. and Wu, J., (2004) High accuracy classification of EEG signal, Intl. Conference on Pattern Recognition, 391–394.
[3]	Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., and Elger, C. E., (2001) Indications of nonlinear determinis-tic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Phys Rev E, 64, 061907.
[4]	Binaghi, E., Ventura, A. D., Rampini, A., and Schettini, R. (1993) Fuzzy reasoning approach to similarity evaluation in image analysis. International Journal of Intelligent Systems, 8, 749-769.
[5]	Tolias, Y. A., Panas, S. M., and Tsoukalas, L. H., (2001) Gen-eralized fuzzy indices for similarity matching, Fuzzy Sets and Systems, 120(2), 255-270.
[6]	Ali Moti Nasrabadi.
[7]	Domenick, D. G. (1998) International 10-20 electrode place-ment system for sleep. http://members.aol.com/aduial/1020sys.html.
[8]	Sabeti, a M., Boostani, a R., Katebi, a S. D., and Price, G. W. (2007) Selection of relevant features for EEG signals classification of schizophrenic patients. 10.1016/j.bspc.03.003.
[9]	übeyli, E. D. Fuzzy similarity index for discrimination of EEG signals. EMBS Annual.
[10]	Gómez, C., et al. (2008) Use of the Higuchi’s fractal dimen-sion for the analysis of MEG recordings from Alzheimer’s disease patients. Med Eng Phys, doi:10.1016/j. medeng-phy.06.010.
[11]	Stam, C. J. (2005) Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field. Clin Neurophysiol, 116, 2266–301.
[12]	Accardo, A., Affinito, M., Carrozzi, M., and Bouquet, F. ( 1997) Use of fractal dimension for the analysis of electroencephalo-graphic time series. Biol Cybern, 77, 339–50.
[13]	Kwang, H. L., Song, Y.-S., and Lee, K.-M. (1994) Similarity measure between fuzzy sets and between elements, Fuzzy Sets and Systems, 62(3), 291–293.