Analysis of the EEG bio-signals during the reading task by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e444" altimg="si5.gif"><mml:mi>D</mml:mi><mml:mi>F</mml:mi><mml:mi>A</mml:mi></mml:math> method

Filho, Florêncio Mendes Oliveira; Cruz, Juan Alberto Leyva; Zebende, Gilney Figueira

doi:10.1016/j.physa.2019.04.035

Cited by 14 publications

(7 citation statements)

References 15 publications

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“…In this case, α DFA is defined as a self-affinity parameter that represents the long-range auto-correlation (see Table 1 below and [26]): The DFA method is also able to identify seasonal components [27][28][29], and the amplitude of F DFA (n) can be applied to classify or distinguish different types of signals [30].…”

Section: Methodsmentioning

confidence: 99%

Effects of Interdependence and Contagion on Crude Oil and Precious Metals According to ρDCCA: A COVID-19 Case Study

et al. 2023

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The energy sector has been the main economic hub in everyone’s lives and in world geopolitics. Consequently, oil, gas, electricity and energy from renewable sources (wind and solar) are traded on the stock market, and all interconnected around the world. On the other hand, a global health crisis, such as COVID-19, can produce a great economic catastrophe. In this scenario, a robust statistical analysis will be performed here with respect to the concept of interdependence and contagion effect. For this project, we chose to study the relationship between the main source of energy (crude oil, WTI and Brent) and two (Gold and Silver) precious metals (which are a safe haven for investment). Therefore, with the novelty of the application of ρDCCA and ΔρDCCA coefficients before and during the COVID-19 crisis (announced by the World Health Organization), the interdependence and the contagion effect were calculated. We verified that COVID-19 had no influence on contagion effect between crude oil in its indexes, WTI and Brent, since they have already shown to be highly interdependent, both before and after the World Health Organization COVID-19 decree. Likewise, COVID-19 had a significant influence on the crude oil and precious metal sectors, which was evident as we identified an increase in its interdependence, with a clearly positive contagion. These results show that COVID-19 imposed a restructuring in the relationship between energy (crude oil) and precious metals. More details will be presented throughout this article.

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Section: Methodsmentioning

confidence: 99%

Effects of Interdependence and Contagion on Crude Oil and Precious Metals According to ρDCCA: A COVID-19 Case Study

et al. 2023

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“…Peng et al (1994) 13 developed the detrended fluctuation analysis (DFA) to analyse the existence of serial dependence (the statistical self-affinity of a signal), with the advantage of being also possible to be used in non-stationary data. Its main advantage is to avoid spurious detection of long-range dependence due to nonstationary data [15][16][17][18][19] . For a given "Y " signal, the algorithm is described as follows:…”

Section: Data Collection and Data Organizationmentioning

confidence: 99%

Networks Analysis Of Brazilian Climate Data Based On The DCCA Cross-Correlation Coefficient

Rodrigues

Filho

Guedes

2022

Preprint

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Climate change is one of the most relevant challenges that the world has to deal with. A better understanding about the behaviour of environmental and atmospheric variables, their long range cross correlation and the way they relate to each other, can provide helpful insights about how the climate is changing. However, such studies are complex and rarely found in the literature, especially dealing with data from the Brazilian territory. In this paper we consider four environmental and atmospheric variables, wind speed, radiation, temperature and humidity, measured in 27 stations (the capital of each of the 26 Brazilian states plus the federal district). We use the detrended fluctuation analysis to evaluate the statistical self-affinity of the signal, the cross correlation coefficient $\rho_{DCCA}$ to quantify the long range cross correlation between stations, and a network analysis that considers the top $10\%$ $\rho_{DCCA}$ values for each atmospheric variable to better understand the cross correlation between stations. The methodology used and combined in this paper represents a step forward in the field of time series and network analysis, that can be applied to other regions, other environmental variables and also to other fields of research. The results of the application are of great importance to better understand the behaviour of environmental and atmospheric variables in the Brazilian territory and to provide helpful insights about climate change and renewable energy production.

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“…Brain coherence refers to the synchronization and organization of patterns of electrical activity in the brain, demonstrating effi cient communication between diff erent brain regions [1][2][3]. This phenomenon is crucial for the proper functioning of the nervous system, allowing the integration of information and the execution of complex tasks.…”

Section: Introductionmentioning

confidence: 99%

“…The results show that there is a clear diff erentiation between the experiments, the autocorrelation function is more concentrated for the subject who did not have the brain trained, although there is a clear separation between the data from the subject with the trained brain and the subjects with the untrained brain. Trained for time scales n < 128 ( f > 1 Hz) [2]. A third study, the most recent (2023), tested the root mean square fl uctuation (RMS) function, highlighting the importance of understanding it in assessing the extent of brain damage and solutions such as rehabilitation or limb replacement using bionic prostheses [9].…”

Section: Introductionmentioning

confidence: 99%

Temporal Coherence in the Synchronization of Brain Electrical Activity Patterns: An Application with the RMS Fluctuation Function

Oliveira Filho,

Zebende

2024

J Biomed Res Environ Sci

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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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Analysis of the EEG bio-signals during the reading task by DFA method

Cited by 14 publications

References 15 publications

Effects of Interdependence and Contagion on Crude Oil and Precious Metals According to ρDCCA: A COVID-19 Case Study

Effects of Interdependence and Contagion on Crude Oil and Precious Metals According to ρDCCA: A COVID-19 Case Study

Networks Analysis Of Brazilian Climate Data Based On The DCCA Cross-Correlation Coefficient

Temporal Coherence in the Synchronization of Brain Electrical Activity Patterns: An Application with the RMS Fluctuation Function

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