Non-linear analysis of the EEG gamma wave in an attention and inhibition test

Análisis no-lineal de la onda gamma del EEG en una prueba de atención e inhibición

Published 2023-12-19

Open | Download
PDF (Spanish) FLIP
Issue
Vol. 20 No. 40 (2023): Table of contents Revista EIA No. 40
Section
Articles
How to Cite
Non-linear analysis of the EEG gamma wave in an attention and inhibition test. (2023). Revista EIA, 20(40), 4007 pp. 1-16. https://doi.org/10.24050/reia.v20i40.1670
DOI

https://doi.org/10.24050/reia.v20i40.1670
Dimensions
PlumX
Citations
Crossref
Citations in Crossref
Scopus
Citations in Scopus
Google Scholar
Europe PMC
Citations in Europe PMC
license
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright statement

The authors exclusively assign to the Universidad EIA, with the power to assign to third parties, all the exploitation rights that derive from the works that are accepted for publication in the Revista EIA, as well as in any product derived from it and, in in particular, those of reproduction, distribution, public communication (including interactive making available) and transformation (including adaptation, modification and, where appropriate, translation), for all types of exploitation (by way of example and not limitation : in paper, electronic, online, computer or audiovisual format, as well as in any other format, even for promotional or advertising purposes and / or for the production of derivative products), for a worldwide territorial scope and for the entire duration of the rights provided for in the current published text of the Intellectual Property Law. This assignment will be made by the authors without the right to any type of remuneration or compensation.

Consequently, the author may not publish or disseminate the works that are selected for publication in the Revista EIA, neither totally nor partially, nor authorize their publication to third parties, without the prior express authorization, requested and granted in writing, from the Univeridad EIA.

Fernando Maureira Cid
Universidad Metropolitana de Ciencias de la Educación - Chile
https://orcid.org/0000-0001-7607-7416
Hernán Díaz Muñoz
Universidad de Santiago - Chile
https://orcid.org/0000-0002-8310-3835
Marcelo Hadweh Briceño
Universidad SEK de Chile
https://orcid.org/0000-0002-5218-3131
Show authors biography

Introduction: In recent decades, the EEG signal has been studied from a non-linear
mathematical perspective, allowing the understanding of cerebral electrical activity as
a complex dynamic system. Objective: analyze the Hurst exponents and correlations of
such exponents in the gamma wave during the resolution of an alternating attention and
interference inhibition task in university students. Methods: The sample consisted of 14
male students of physical education. The brain-interface device Emotiv Epoc® was used
to evaluate the electrical activity of the brain, the symbols and digits test was applied
to evaluate the alternating attention, and the Stroop words and colors test were used to
inhibit interference Results: Of the seven subjects who solved the alternating attention
test, one presented a greater tendency to chaos in the left hemisphere, four revealed a
greater tendency to chaos in the right hemisphere and two did not present a definite
tendency. Of the seven subjects who solved the interference inhibition test, five presented
variations in the mean H between the three Stroop plates, especially in the temporal
region. The mean of the H exponents in both tests was less than 0,5. Conclusions:
During the attention test, a greater chaos of brain electrical activity is observed, without
correlations between the regions studied. During the inhibition test, H modifications do
not present definite patterns towards order or chaos.

Article visits 320 | PDF visits 200

Downloads

Download data is not yet available.
  1. Aldana, C.; Buitrago, E. (2013). Actualidad en la investigación de electroencefalograma-resonancia magnética funcional simultáneos en el estudio de epilepsia y dolor. Revista Cubana de Investigaciones Biomédicas, 32(1), pp. 29-47.
  2. Ardila, A.; Ostrosky, F. (2008). Desarrollo histórico de las funciones ejecutivas. Revista de Neuropsicología, Neuropsiquiatría y Neurociencia, 8(1): pp. 1-21.
  3. Ardila, A.; Ostrosky, F. (2012). Guía para el diagnóstico neuropsicológico, Miami: Florida, Internacional University.
  4. Bear, M.; Connors, B.; Paradiso, M. (2016). Neurociencia, la exploración del cerebro, 4a ed., Madrid: Wolters Kluver.
  5. Cerquera, A.; Arns, M.; Buitrago, E.; Gutiérrez, R.; Freund, J. (2012). Nonlinear dynamics measures applied to EEG recordings of patients with attention deficit/hyperactivity disorder: quantifying the effects of a neurofeedback treatment. Conf Proc IEEE Eng Med Biol Soc, 2012: pp. 1057-1060. https://doi.org/10.1109/EMBC.2012.6346116
  6. Colombo, M.; Wei, Y.; Ramautar, J.; Linkenkaer-Hansen, K.; Tagliazucchi, E.; Van Someren, E. (2016). More severe insomnia complaints in people with stronger long-range temporal correlations in wake resting-state EEG. Front Physiol, 7. https://doi.org/10.3389/fphys.2016.00576
  7. Díaz, H.; Maureira, F.; Cohen, E.; Córdova, F.; Palominos, F.; Otárola, J.; Cañete, L. (2015). Individual differences in the orden/chaos balance of the brain self-organization. Annals of Data Science, 2(4), pp. 421-438. https://doi.org/10.1007/s40745-015-0051-y
  8. Díaz, H.; Maureira, F.; Córdova, F. (2017). Temporal scaling and inter-individual hemispheric asymmetry of chaos estimation from EEG time series. Procedia Computer Science, 122, pp. 339-345. https://doi.org/10.1016/j.procs.2017.11.378
  9. Díaz, H.; Maureira, F.; Córdova, F.; Palominos, F. (2017). Long-range linear correlation and nonlinear chaos estimation differentially characterizes functional connectivity and organization of the brain EEG. Procedia Computer Science, 122, pp. 857-864. https://doi.org/10.1016/j.procs.2017.11.447
  10. Díaz, H.; Maureira, F.; Flores, E.; Cifuentes, H.; Córdova, F. (2019). Synchronizing oscillatory chaos in the brain. Procedia Computer Science, 162, pp. 982-989. https://doi.org/10.1016/j.procs.2019.12.076
  11. Díaz, H.; Maureira, F.; Otárola, J.; Rojas, R.; Alarcón, O.; Cañete, L. (2019). EEG Beta band frequency domain evaluation for assessing stress and anxiety in resting, eyes closed, basal conditions. Procedia Computer Science, 162, pp. 974-981. https://doi.org/10.1016/j.procs.2019.12.075
  12. Flores, E.; Maureira, F.; Díaz, H.; Navarro, B. (2020). Modificaciones neurofisiológicas de ondas beta durante un test atencional tras una intervención de ejercicio físico. Cultura, Ciencia y Deporte, 15(44), pp. 201-211.
  13. Flores, F.; Maureira, F.; Díaz, H.; Navarro, B.; Gavotto, O.; Matheu, A. (2019). Efectos de una sesión de ejercicio físico sobre la actividad neurofisiológica durante la resolución de una prueba de atención selectiva. Retos, 36, pp. 390-396
  14. Gupta, A.; Singh, P.; Karlekar, M. (2018). A novel signal modeling approach for classification of seizure and seizure-free EEG signals. IEEE Trans Neural Syst Rehabil Eng, 26(5), pp. 925-935. https://doi.org/10.1109/TNSRE.2018.2818123
  15. Jausovec, N.; Jausovec, K. (2010). Resting brain activity: differences between genders. Neuropsychologia, 48(13), pp. 3918-3925. https://doi.org/10.1016/j.neuropsychologia.2010.09.020
  16. Jia, X.; Kohn, A. (2011). Gamma rhythms in the brain. Plos Biology, 9(4). https://doi.org/10.1371/journal.pbio.1001045
  17. Klonowski, W. (2016). Fractal analysis of electroencephalographic time series (EEG Signals). In Di Leva A. editor. The fractal geometry of the brain. New York: Springer-Verlag, pp.413-429.
  18. Lozano, A.; Ostrosky, F. (2011). Desarrollo de las funciones ejecutivas y de la corteza prefrontal. Revista de Neuropsicología, Neuropsiquiatría y Neurociencia, 11(1): 159-172.
  19. Maureira, F; Aravena, C.; Bahamondes, V.; Díaz, H.; Flores, E.; Gálvez, C.; Hadweh, M.; Maureira, Y.; Véliz, C. (2017). ¿Qué es la inteligencia?, Madrid: Bubok Publishing.
  20. Maureira, F. (2018). Principios de neuroeducación física. 2° ed. Madrid: Bubok Publishing.
  21. Maureira, F.; Flores, E. (2016). Principios de neuropsicobiología. Valencia: Obrapropia.
  22. Maureira, F.; Flores, E. (2020). Estabilidad de la actividad eléctrica no lineal durante condiciones basales con los ojos cerrados. Revista Cubana de Investigaciones Biomédicas, 39(3).
  23. Maureira, F.; Flores, E.; Díaz, H. (2020). Diferencias inter e intra-hemisfericas de dinámicas no lineales de la señal de EEG durante la resolución de una prueba de atención. Revista de la Facultad de Medicina, 68(4), pp. 577-585. https://doi.org/10.15446/revfacmed.v68n4.76046.
  24. Munia, T.; Haider, A.; Schneider, C.; Romanick, M.; Fazel-Rezai, R. (2017). A novel EEG based spectral analysis of persistent brain function alteration in athletes with concussion history. Sci Rep., 7(1), 17221. https://doi.org/10.1038/s41598-017-17414-x
  25. Pikovsky, A.; Rosenblum, M.; Kurths, J. (2001). Synchronization: a universal concept in nonlinear sciences, Cambridge: Cambridge University Press.
  26. Portellano, J. (2005). Introducción a la neuropsicología, Madrid: McGraw-Hill Interamericana de España.
  27. Portnova, G.; Atanov, M. (2016). Age-dependent changes of the EEG data: comparative study of correlation dimensión D2, spectral analysis, peak alpha frequency and stability of rhythms. International Journal of Innovative Research in Computer Science & Technology, 4(2), pp. 56-61.
  28. Racz, F.; Stylianou, O.; Mukli, P.; Eke, A. (2018). Multifractal dynamic functional connectivity in the resting-state brain. Front Physiol., 9, 1704. https://doi.org/10.3389/fphys.2018.01704
  29. Rahmani, B.; Wong, C.; Norouzzadeh, P.; Bodurka, J.; McKinney, B. (2018). Dynamical Hurst analysis identifies EEG channel differences between PTSD and healthy controls. PLoS One, 13(7). https://doi.org/10.1371/journal.pone.0199144
  30. Smith, A. (1973). Symbol digit modalities test manual, Los Angeles: Western Psychological Services.
  31. Stroop J. (1935). Studies of interference in serial verbal reaction. Journal Expedients Psychology, 18, pp. 643-662.
  32. Tarnopolsky, M. (2018). Correlation between the Hurst exponent and the maximal Lyapunov exponent: Examining some low-dimensional conservative maps. Physica A: Statistical Mechanics and its Applications, 490, pp. 834-844. https://doi.org/10.1016/j.physa.2017.08.159
  33. World Medical Asociation. (2013). World Medical Association Declaration of Helsinki Ethical Principles for Medical Research Involving Human Subjects. JAMA, 310(20), pp. 2191-2194. https://doi.org/10.1001/jama.2013.281053
  34. Zarjam, P.; Epps, J.; Lovell, N.; Chen, F. (2012). Characterization of memory load in an arithmetic task using non-linear analysis of EEG signals. Conf Proc IEEE Eng Med Biol Soc., pp. 3519-3522. https://doi.org/10.1109/EMBC.2012.6346725

Information

Sistema OJS 3.4.0.7 - Metabiblioteca |