Method of evaluation of the minimal sample size for acoustical signal therapy monitored via electroencephalographic activity of human brain
The aim of the study. Improvement of the preparation to the acoustical signal therapy test or experiment of electroencephalographic activity of human brain and validation of the specified test results.
The problem to be solved. Estimation of the minimal possible sample size for maintaining needed research accuracy in the research field of the electroencephalographic activity of human brain via monitoring of the brainwave patterns during exposure to the musical signal.
Main scientific results. New method for selection minimal passable sample size for brainwave pattern studies is presented. Example of application of method for one rhythm of the brainwaves (delta-rhythm) is shown. Perspective way of obtaining clinically valuable differences between test group results was acquired. Differences between mean values for groups of results of different types of music and stress factor exposure are presented.
The area of practical use of the research results. Research facilities dedicated to the study of electroencephalographic activity of human brain and medical facilities and institutions, dedicated to the treatment of pathologies of the central nervous system, brain damage, stress, and progressive post-stress action psychological state restoration.
An innovative technological product. Dedicated method for quick estimation of minimal passable sample size for brainwave pattern studies, which is recommended for usage in the studies of the implementation of music therapy.
The area of application of an innovative technological product. Electroencephalographic activity of human brain study via brainwave pattern research. Clinical practice of application of a music therapy.
Rahman, M., Karwowski, W., Fafrowicz, M., Hancock, P. A. (2019). Neuroergonomics Applications of Electroencephalography in Physical Activities: A Systematic Review. Frontiers in Human Neuroscience, 13, 21. doi: http://doi.org/10.3389/fnhum.2019.00182
Ernst, N., da Silva Fernando, L. (2005). Electroencephalography : Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins, 1309.
Raglio, A., Attardo, L., Gontero, G., Rollino, S., Groppo, E., Granieri, E. (2015). Effects of music and music therapy on mood in neurological patients. World Journal of Psychiatry, 5 (1), 68–78. doi: http://doi.org/10.5498/wjp.v5.i1.68
Derya Übeyli, E. (2009). Statistics over features: EEG signals analysis. Computers in Biology and Medicine, 39 (8), 733–741. doi: http://doi.org/10.1016/j.compbiomed.2009.06.001
Bullock, M., Jackson, G. D., Abbott, D. F. (2021). Artifact Reduction in Simultaneous EEG-fMRI: A Systematic Review of Methods and Contemporary Usage. Frontiers in Neurology, 12, 622719. doi: http://doi.org/10.3389/fneur.2021.622719
Urigüen, J. A., García-Zapirain, B., Artieda, J., Iriarte, J., Valencia, M. (2017). Comparison of background EEG activity of different groups of patients with idiopathic epilepsy using Shannon spectral entropy and cluster-based permutation statistical testing. PLOS ONE, 12 (9), e0184044. doi: http://doi.org/10.1371/journal.pone.0184044
Yoshinaga, H., Ohtsuka, Y., Tamai, K., Tamura, I., Ito, M., Ohmori, I., Oka, E. (2004). EEG in childhood absence epilepsy. Seizure, 13 (5), 296–302. doi: http://doi.org/10.1016/s1059-1311(03)00196-1
Indira, V., Rasanthakumari, R., Sugumaran, V. (2012). Sample size determination for classification of eeg signals using power analysis in machine learning approach. International Journal of Advanced Research in Engineering & Management, 3 (1), 1–9.
Hwang, D., Schmitt, W., Stephanopoulos, G., Stephanopoulos, G. (2002). Determination of Minimum Sample Size and Discriminatory Expression Patterns. Bioinformatics, 18 (9), 1184–1193. doi: http://doi.org/10.1093/bioinformatics/18.9.1184
Luh, W.-M., Olejnik, S., Guo, J.-H. (2008). Sample Size Determination for One-and Two-Sample Trimmed Mean Tests. The Journal of Experimental Education, 77 (2), 167–184. doi: http://doi.org/10.3200/jexe.77.2.167-184
Tikhova, G. P. (2014). Planning clinical research. Question No. 1: How to calculate enough sample volume? Regionarnaia anesteziia i lechenie ostroi boli, 8 (3), 57–63.
Liashko, D. (2020). Methods of music therapy and experimental study of bioelectrical activity of students’ brains while listening to the musical composition of the audible frequency spectrum. ScienceRise, 6, 74–80. doi: http://doi.org/10.21303/2313-8416.2020.001561
Sistema 10–20. Raspolozhenie elektrodov na golove (2021). CMI Brain Research. Available at: https://cmi.to/ээг/система-10-20/
Taherdoost, H. (2016). Sampling Methods in Research Methodology; How to Choose a Sampling Technique for Research. International Journal of Academic Accounting, Finance & Management Research., 5, 18–27. doi: http://doi.org/10.2139/ssrn.3205035
The Probable Error of a Mean (1908). Biometrika, 6 (1), 1–25. doi: http://doi.org/10.2307/2331554
Pareniuk, D. V., Rudenka, K. L., Didkovskyi, V. S., Naida, S. A., Timen, H. E. (2018). The Study of Implementation of the Otoacoustic Emission for Registration of the Medicamentous Influence on the Auditory Channel of Guinea Pigs. Microsystems, Electronics and Acoustics, 23 (4), 74–81. doi: http://doi.org/10.20535/2523-4455.2018.23.4.134457
Naida, S. A., Pareniuk, D. V., Tіmen, G. E., Rudenka, K. L. (2017). Otoacoustic emission as a diagnostic methodin experimental sensorineural hearing loss. Zhurnal vushnih, nosovih і gorlovih hvorob, 5, 13–20. Available at: http://www.lorlife.kiev.ua/2017/2017_5_13.pdf
Tian, Y., Ma, L., Xu, W., Chen, S. (2020). The Influence of Listening to Music on Adults with Left-behind Experience Revealed by EEG-based Connectivity. Scientific Reports, 10 (1), 7575. doi: http://doi.org/10.1038/s41598-020-64381-x
Serdar, C. C., Cihan, M., Yücel, D., Serdar, M. A. (2021). Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies. Biochemia Medica, 31 (1), 27–53. doi: http://doi.org/10.11613/bm.2021.010502
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