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

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  • Oliveira Filho, F.M.
  • Leyva Cruz, J.A.
  • Zebende, G.F.

Abstract

The process of reading a specific text is considered complex and little known in neuroscience, since it involves the vision, memory, motor control, learning, among others. In this sense, an excellent possibility to study the brain activity in the reading task can be achieved by the analysis of the multi-channel Electroencephalogram (EEG) and also with new statistical methods, like the detrended fluctuation analysis method (DFA). In this paper it will be proposed a model to analyze the brain activity in the reading task, performed by two subjects using a 22-channels EEG (NEUROMAP® model EQSA260). In order to test our model, two adults subjects (graduates) were tested here. These subjects were arranged in a chair facing a panel with the specific text, excluding involuntary movements that activated regions of the brain that were not being stimulated by reading. For the first subject, chosen at random, the text was presented before the task for understanding and some memorization. For the other subject the text was presented at the time of task. For the signal processing we chose 11 bio-electrodes located at the frontal, parietal, temporal and occipital regions of the brain. Therefore, to treat these non-stationary bio-signals we must apply robust and modern statistical techniques. With this objective, DFA method was applied in order to analyze the FDFA(n) fluctuation function in multi-channel EEG bio-sensors, more specifically the difference of its logarithm, i.e., ΔlogFDFA. The results show that the use of this new function can be useful for brain activities. This paper, as we shall see here, is an initial contribution for EEG data analyze, that would be of medical interest, mainly in neuroscience area.

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  • Oliveira Filho, F.M. & Leyva Cruz, J.A. & Zebende, G.F., 2019. "Analysis of the EEG bio-signals during the reading task by DFA method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 664-671.
    • Handle: RePEc:eee:phsmap:v:525:y:2019:i:c:p:664-671
    DOI: 10.1016/j.physa.2019.04.035
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    1. López, J.L. & Veleva, L., 2022. "2D-DFA as a tool for non-destructive characterisation of copper surface exposed to substitute ocean water," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    2. Arias-Calluari, Karina & Najafi, Morteza. N. & Harré, Michael S. & Tang, Yaoyue & Alonso-Marroquin, Fernando, 2022. "Testing stationarity of the detrended price return in stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    3. Filho, F.M. Oliveira & Ribeiro, F.F. & Cruz, J.A. Leyva & de Castro, A.P. Nunes & Zebende, G.F., 2023. "Statistical study of the EEG in motor tasks (real and imaginary)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).
    4. Karina Arias-Calluari & Morteza. N. Najafi & Michael S. Harr'e & Fernando Alonso-Marroquin, 2019. "Stationarity of the detrended price return in stock markets," Papers 1910.01034, arXiv.org, revised Aug 2020.
    5. da Silva Filho, A.M. & Zebende, G.F. & Guedes, E.F., 2021. "Analysis of intentional lethal violent crimes: A sliding windows approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    6. Thiago Pires Santana & Nicole Horta & Catarina Revez & Rui Manuel Teixeira Santos Dias & Gilney Figueira Zebende, 2023. "Effects of Interdependence and Contagion on Crude Oil and Precious Metals According to ρ DCCA : A COVID-19 Case Study," Sustainability, MDPI, vol. 15(5), pages 1-12, February.

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