Florencio Mendes Oliveira Filho* and Gilney Figueira Zebende
Volume5-Issue2
Dates: Received: 2024-02-06 | Accepted: 2024-02-26 | Published: 2024-02-29
Pages: 221-226
Abstract
In this article, we performed a preliminary test with the mean square fluctuation (RMS) function, using EEG data, in which the task consisted of the response given to the real/imagined motor stimulus. All steps, up to the configuration of the RMS function, relied on information from subject S010, available in the Physionet database. We analyzed 12 of the 64 channels and four stimuli. We evaluated the amplitude of channels 32 and 37 in relation to the others (11, 22, 24, 43, 44, 49, 54, 61, 63 and 64). We quantitatively observed similarities when the brain perfoRMS the same real/imagined motor task, and that the timing of the amplitude changes with increasing n scale (time scales). In all experiments (S010R03, S010I03, S010R04, S010I04, S010R05, S010I05, S010R06 and S010I06), channels 32 x 22(∆logF32,22) and 37 x 24 (∆logF37,24), for n > 100 (60 seconds), were smaller than the others, showing so channels 32 and 37 (left/right hemisphere) have the greatest fluctuation and differences between them. From data processing (.EDF) to visualization of the curves (FDFA / ∆log), we concluded that it is possible to replicate the test with more channels and subjects, as well as investigate other types of activities of the human brain with signals extracted from the EEG.
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DOI: 10.37871/jbres1885
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© 2024 Oliveira Filho FM, et al. Distributed under Creative Commons CC-BY 4.0 
How to cite this article
Oliveira Filho FM, Zebende GF. Temporal Coherence in the Synchronization of Brain Electrical Activity Patterns: An Application with the RMS Fluctuation Function. J Biomed Res Environ Sci. 2024 Feb 29; 5(2): 221-226. doi: 10.37871/jbres1885, Article ID: JBRES1885, Available at: https://www.jelsciences.com/articles/jbres1885.pdf
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References
- Zebende GF, Oliveira Filho FM, Leyva Cruz JA. Auto-correlation in the motor/imaginary human EEG signals: A vision about the FDFA fluctuations. PLoS One. 2017 Sep 14;12(9):e0183121. doi: 10.1371/journal.pone.0183121. PMID: 28910294; PMCID: PMC5598924.
- Oliveira Filho FM, Leyva Cruz JA, and Zebende GF. Analysis of the EEG bio-signals during the reading task by DFA method. Physica A. 2019;525:664-671.
- Lee JS, Yang BH, Lee JH, Choi JH, Choi IG, Kim SB. Detrended fluctuation analysis of resting EEG in depressed outpatients and healthy controls. Clin Neurophysiol. 2007 Nov;118(11):2489-96. doi: 10.1016/j.clinph.2007.08.001. Epub 2007 Sep 24. PMID: 17890151.
- Kiloh LG, McComas AJ, Osselton JW. Clinical electroencephalography. 2013.
- Binnie CD, Prior PF. Electroencephalography. J Neurol Neurosurg Psychiatry. 1994;57(11):1308-19. doi: 10.1136/jnnp.57.11.1308. PMID: 7964803; PMCID: PMC1073178.
- Davidson RJ, Jackson DC, and Larson CL. Human electroencephalography. 2000.
- Chen Z. Effect of nonstationarities on detrended fluctuation analysis. Phys Rev E. 2001;65:1–14.
- Yao D. A method to standardize a reference of scalp EEG recordings to a point at infinity. Physiol Meas. 2001 Nov;22(4):693-711. doi: 10.1088/0967-3334/22/4/305. PMID: 11761077.
- Oliveira Filho F, Ribeiro F, Cruz JL, de Castro AN, and Zebende G. Statistical study of the eeg in motor tasks (real and imaginary). Physica A: Statistical Mechanics and its Applications. 2023a;622:128802.
- Zebende GF. DCCA cross-correlation coefficient: Quantifying level of cross- correlation. Physica A. 2011;390(4):614–618.
- Peng CK, Buldyrev SV, Havlin S, Simons M, Stanley HE, Goldberger AL. Mosaic organization of DNA nucleotides. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics. 1994 Feb;49(2):1685-9. doi: 10.1103/physreve.49.1685. PMID: 9961383.
- Mesquita VB, Oliveira Filho FM, Rodrigues PC. Detection of crossover points in detrended fluctuation analysis: an application to EEG signals of patients with epilepsy. Bioinformatics. 2021 Jun 9;37(9):1278-1284. doi: 10.1093/bioinformatics/btaa955. PMID: 34107041.
- Oliveira Filho EA.Self-regulation in electroencephalographic signals during an arithmetic performance test: an approach with an rms fluctuation function. 2021. 15921091210.
- Guedes EF, Brito AA, Oliveira Filho FM, Fernandez BF, de Castro APN, da Silva Filho AM, Zebende GF. Statistical test for ΔρDCCA : Methods and data. Data Brief. 2018 Mar 22;18:795-798. doi: 10.1016/j.dib.2018.03.080. PMID: 30140718; PMCID: PMC6103976.
- Vassoler R, Zebende G. Cross-correlation coefficient apply in time series of air temperature and air relative humidity. Physica A. 2012;391(7):2438-2443.
- Brito AA, Araújo HA, Zebende GF. Detrended Multiple Cross-Correlation Coefficient applied to solar radiation, air temperature and relative humidity. Sci Rep. 2019 Dec 24;9(1):19764. doi: 10.1038/s41598-019-56114-6. PMID: 31874976; PMCID: PMC6930259.
- Oliveira Filho FM, Guedes EF, Rodrigues PC. Networks analysis of Brazilian climate data based on the DCCA cross-correlation coefficient. PLoS One. 2023 Sep 15;18(9):e0290838. doi: 10.1371/journal.pone.0290838. PMID: 37713368; PMCID: PMC10503753.
- Schalk G, McFarland DJ, Hinterberger T, Birbaumer N, Wolpaw JR. BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans Biomed Eng. 2004 Jun;51(6):1034-43. doi: 10.1109/TBME.2004.827072. PMID: 15188875.
