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IJSTR >> Volume 8 - Issue 11, November 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Estimation Of Hazard In Human Brain Signal Using Exponential Distribution

[Full Text]



Ajaya Kumar Mahapatra, Sandhyalati Behera, Brijesh Kumar Jha, Mihir Narayan Mohanty*



ECG—Electrocardiography, EMG- Electromyography, EOG, EEG.



The physical parameters of human being are most complex that needs to compensate with the stochastic process. Out of all other signals like ECG, EMG, EOG, EEG signal acquisition and analysis are a difficult task. At the time of acquisition of EEG signal artifact may occur due to muscular and eye blinking which is hazardous. In this paper the artifact is considered by the hazard function and is estimated. For this paper, human brain tracks the hazard on momentary basis and can observe these variations. Early to estimation, the parameter distribution is performed and chosen as exponential distribution and the errors have been shown in result section to track the artifacts for further process.



[1] [1] S. K. Herbst, L. Fiedler, and J. Obleser, "Tracking temporal hazard in the human electroencephalogram using a forward encoding model," eNeuro, vol. 5, 2018.
[2] [2] N. Nazmi, S. A. Mazlan, H. Zamzuri, and M. A. A. Rahman, "Fitting distribution for electromyography and electroencephalography signals based on goodness-of-fit tests," Procedia Computer Science, vol. 76, pp. 468-473, 2015.
[3] [3] R. Lenz, "Generalized extreme value distributions, information geometry and sharpness functions for microscopy images," in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014, pp. 2848-2852.
[4] [4] O. Aydemir, "Classifying Various EMG and EOG Artifacts in EEG Signals," Przeglad Elektrotechniczny, vol. 88, pp. 218-222, 11/01 2012.
[5] [5] M. Teplan, "Fundamentals of EEG measurement," Measurement science review, vol. 2, pp. 1-11, 2002.
[6] [6] S. Behera and M. N. Mohanty, "A Statistical Approach for Ocular Artifact Removal in Brain Signals," in 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA), 2018, pp. 500-503.
[7] [7] M. R. Lakshmi, T. Prasad, and D. V. C. Prakash, "Survey on EEG signal processing methods," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 4, 2014.
[8] [8] P. LeVan, E. Urrestarazu, and J. Gotman, "A system for automatic artifact removal in ictal scalp EEG based on independent component analysis and Bayesian classification," Clinical Neurophysiology, vol. 117, pp. 912-927, 2006.
[9] [9] N. Mammone, F. La Foresta, and F. C. Morabito, "Automatic artifact rejection from multichannel scalp EEG by wavelet ICA," IEEE Sensors Journal, vol. 12, pp. 533-542, 2011.
[10] [10] M. Arshad and N. Misra, "On estimating the scale parameter of the selected uniform population under the entropy loss function," Brazilian Journal of Probability and Statistics, vol. 31, pp. 303-319, 2017.
[11] [11] M. Arshad, N. Misra, and P. Vellaisamy, "Estimation after selection from gamma populations with unequal known shape parameters," Journal of Statistical Theory and Practice, vol. 9, pp. 395-418, 2015.
[12] [12] H. Sackrowitz and E. Samuel‐Cahn, "Estimation of the mean of a selected negative exponential population," Journal of the Royal Statistical Society: Series B (Methodological), vol. 46, pp. 242-249, 1984.
[13] [13] S. Kumar, A. K. Mahapatra, and P. Vellaisamy, "Reliability estimation of the selected exponential populations," Statistics & Probability Letters, vol. 79, pp. 1372-1377, 2009.
[14] [14] M. A. Klados and P. D. Bamidis, "A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques," Data in brief, vol. 8, pp. 1004-1006, 2016.