AR modeling as EEG spectral analysis on prostration

Salleh, N.A. and Lim, K.S. and Ibrahim, F. (2009) AR modeling as EEG spectral analysis on prostration. In: International Conference for Technical Postgraduates 2009, TECHPOS 2009, 2009, Kuala Lumpur.

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Abstract

Autoregressive (AR) modeling involves selection of an appropriate model order and the estimation of model parameters from the available data. Spectral estimation is then carried out using the model parameter. This spectral analysis is chosen as an alternative method to FFT in analysis of brain wave. Muslims prayer, termed as "Salat" in the Arabic language is a worshipping act which encompasses both physical movement of the body as well as silent Quranic recitation through mind and soul. The various positions in Salat include standing, prostrating, bowing and sitting. Prostrating is one of the unique position in salat which is believed can promote a relaxation effect to human body. In this study, AR was used to analyze the EEG signals during salat on prostrating position. The result shows that prostrating during salat generated higher alpha relative power (RPα) as compare with mimic prostration. This finding concludes that prostration, one unique position in salat may promote a remarkable relaxation state to human mind and body.

Item Type:	Conference or Workshop Item (Paper)
Funders:	UNSPECIFIED
Additional Information:	Conference code: 79876 Export Date: 29 January 2014 Source: Scopus Art. No.: 5412056 doi: 10.1109/TECHPOS.2009.5412056 Language of Original Document: English Correspondence Address: Salleh, N. A.; Dept of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia References: Appelgate, E.J., (1995) The Anatomy and Physiology Learning System, , 1st ed. USA: W.B. Sounders Company; Ito, S., Mitsukura, Y., Fukumi, M., Akamatsu, N., A feature extraction of the EEG during listening to the music using the Factor Analysis networks (2003) IEEE, Proceeding of the International Joint Conference, 3, pp. 2263-2267; Teplan, M., Fundamentals of EEG measurement (2002) Measurement Science Review, 2 (2), pp. 1-11; Sanei, A., Chambers, J.A., (2007) EEG Signal Processing, , UK: John Wiley and Sons, Ltd; Dimsdale, J.E., Psychological stress and cardiovascular disease (2008) Journal of the American College of Cardiology, 51 (13), pp. 1237-1246; Shaharom, M.H., (2007) 7-Day Stress Relief Plan: Your Road to Recovery. Putrajaya: CERT Publication; Stojanovich, L., Marisavljevich, D., Stress as a trigger of autoimmune disease (2008) Autoimmunity Reviews, 7, pp. 209-213; Banquet, J.P., Spectral analysis of the EEG in Meditation (1973) Electroencephalography and Clinical Neurophysiology, 35 (2), pp. 143-151; Kasamatsu, A., Hirai, T., An electroencephalographic study on the Zen meditation (1996) Folia Psychiatrica et Neurologica Japonica, 20, pp. 315-336; Arambula, P., Peper, E., Kawakami, M., Gibney, K.H., The physiology Correlates of Kundalini Yoga meditation: A study of a Yoga master (2001) Applied Psychophysiology and Biofeedback, 26 (2), pp. 147-153; Ibrahim, F., Wan Abas, W.A.B., Ng, S.C., (2008) Salat: Benefit from Science Perspective, , Kuala Lumpur. Department of Biomedical Engineering, University Malaya; Reza, M.F., Urakami, Y., Mano, Y., Evaluation a new physical exercise taken from. Salat (Prayer) as a short-duration and frequent physical activity in the rehabilitation of geriatric and disabled patients (2002) Annals of Saudi Medicine, 22, pp. 3-4; Takalo, R., Hytti, H., Ihalainen, H., Tutorial on univariate autoregressive spectral analysis export (2006) The Journal of Clinical Monitoring and Computing, 20 (5), pp. 379-379; Spyers-Ashby, J.M., Bain, P.G., Roberts, S.J., A comparison of fast fourier transform (FFT) and autoregressive (AR) spectral estimation techniques for the analysis of tremor data (1998) Journal of Neuroscience Methods, 83, pp. 35-43; Akay, M., (1994) Biomedical Signal Processing, , Academic Press; Faust, O., Acharya, R.U., Allen, A.R., Lin, C.M., Analysis of EEG signals during epileptic and alcoholic states using AR modeling techniques (2008) ITBM-RBM, 29, pp. 44-52; Liang, N., Saratchandran, P., Huang, G., Sundrarajan, N., Classification of mental tasks from. EEG signals using extreme learning machine (2006) International Journal of Neural System, 16 (1), pp. 29-38; Amodio, P., Orsato, R., Marchetti, P., Schiff, S., Poci, C., Angeli, P., Gatta, A., Toffolo, C.M., Electroencephalographic analysis for the assessment of hepatic encephalopathy: Comparison, of non-parametric and parametric spectral estimation techniques (2009) Clinical Neurophysiology, 36 (2), pp. 107-115; Hagemman, D., Naumann, E., The effects of ocular artifacts on (lateralized) broadband power in the EEG (2001) Clinical Neurophysiology, 112, pp. 215-231; Goncharova, I.I., McFarland, D.J., Vaughan, T.M., Wolpaw, J.R., EMG contamination of EEG: Spectral and topographical characteristics (2003) Clinical Neurophysiology, 114, pp. 1580-1593; Mima, T., Ohara, S., Nagamine, T., Cortical-muscular coherence (2002) International Congress Series, 1226, pp. 109-119
Uncontrolled Keywords:	Alternative methods, Appropriate models, Arabic languages, Autoregressive modeling, Brain wave, EEG signals, Human bodies, Human mind, Model parameters, Physical movements, Relaxation effect, Spectral analysis, Spectral Estimation, Electroencephalography, Parameter estimation, Psychophysiology, Spectrum analysis, Spectrum analyzers, Medicine
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:	18 Feb 2014 01:10
Last Modified:	01 Nov 2017 04:11
URI:	http://eprints.um.edu.my/id/eprint/9260

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