The Linear and Nonlinear Indices of Electroencephalography Change in the Stroop Color and Word Test
Abstract
Introduction: This study evaluated the brain activity based on the linear and nonlinear features of surface electroencephalography (EEG) in the Stroop Color and Word Test (SCWT) and the effect of learning in the test response and related EEG features.
Materials and Methods: A total of 21 women and 19 men with physical and mental health participated in this study. Four stages of this SCWT, consistently in the first and second stages and inconsistently in the third and fourth stages, were taken twice by the participants with a 10-min interval. Besides, EEG recording was simultaneously taken for 1 minute at each stage.
Results: The number of correct responses in the inconsistent stages was lower than that in the consistent stages, while the delay of correct responses was more in the consistent stages. EEG features showed that the relative power band of alpha 1 (8-10 Hz) frequency reduced during the test compared to the resting state. In contrast, the gamma 2 (40-50 Hz) frequency band showed a significant increase. There was no significant difference between various stages of the test and between two repetitions in the test indices and EEG features.
Conclusion: Compared to the resting state, the relative power of alpha 1 and gamma 2 frequency bands changed during SCWT without considering the stage of the test.
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29. Song Y, Hakoda Y. An fMRI study of the functional mechanisms of Stroop/reverse-Stroop effects. Behavioral Brain Research. 2015; 290:187-96. [DOI:10.1016/j.bbr.2015.04.0
2. Zarghi A, Zali A, Tehranidost M, Zarindast MR, Khodadadi SM. [Application of cognitive computerized test in assessment of neuro-cognitive domain (Persian)]. Pajoohande. 2011; 16(5):245-341. http://pajoohande.sbmu.ac.ir/article-1-1208-fa.html
3. Soleimani M, Yousefi R, Ghazanfarianpour S. The role of intelligence profiles and executive functions (selective attention and switching) in predicting creativity components. Journal of Modern Rehabilitation. 2020; 14(3):177-90. https://jmr.tums.ac.ir/index.php/jmr/article/view/322
4. Adleman NE, Menon V, Blasey CM, White CD, Warsofsky IS, Glover GH, et al. A developmental fMRI study of the Stroop color-word task. NeuroImage. 2002; 16(1):61-75. [DOI:10.1006/nimg.2001.1046] [PMID]
5. Stevens C, Bavelier D. The role of selective attention on academic foundations: A cognitive neuroscience perspective. Developmental Cognitive Neuroscience. 2012; 2 Suppl 1(Suppl 1):S30-48. [DOI:10.1016/j.dcn.2011.11.001] [PMID] [PMCID]
6. Liu Y, Bengson J, Huang H, Mangun GR, Ding M. Top-down modulation of neural activity in anticipatory visual attention: Control mechanisms revealed by simultaneous EEG-fMRI. Cerebral Cortex. 2016; 26(2):517-29. [PMID]
7. Atchley R, Klee D, Oken B. EEG frequency changes prior to making errors in an easy Stroop task. Frontiers in Human Neuroscience. 2017; 11:521. [DOI:10.3389/fnhum.2017.00521] [PMID] [PMCID]
8. Renaud P, Blondin JP. The stress of Stroop performance: Physiological and emotional responses to color-word interference, task pacing, and pacing speed. International Journal of Psychophysiology. 1997; 27(2):87-97. [DOI:10.1016/S0167-8760(97)00049-4]
9. Schoffelen JM, Gross J. Source connectivity analysis with MEG and EEG. Human Brain Mapping. 2009; 30(6):1857-65. [DOI:10.1002/hbm.20745] [PMID] [PMCID]
10. Khemapathumak P, Lookhanumanchao S, Sittiprapaporn P. EEG power spectra during stroop color word task training in obese patients. Paper presented at: 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology. 27-30 June 2017; Phuket, Thailand. [DOI:10.1109/ECTICon.2017.8096165]
11. Shriram R, Baskar VV, Martin B, Sundhararajan M, Daimiwa N. Statistical analysis of connectivity measures of electroencephalogram during congruent and incongruent stroop task. International Journal of Pure and Applied Mathematics. 2018; 118(17):107-22. https://www.acadpubl.eu/jsi/2018-118-16-17/articles/17/8.pdf
12. Schack B, Chen AC, Mescha S, Witte H. Instantaneous EEG coherence analysis during the Stroop task. Clinical Neurophysiology. 1999; 110(8):1410-26. [DOI:10.1016/S1388-2457(99)00111-X]
13. Hou X, Liu Y, Sourina O, Tan YRE, Wang L, Mueller-Wittig W. EEG based stress monitoring. Paper presented at: IEEE International Conference on Systems, Man, and Cybernetics. 9-12 October 2015; Hong Kong, China. [DOI:10.1109/SMC.2015.540]
14. Pooravari M, Dehghani M, Salehi S, Habibi M. Confirmatory factor analysis of Persian version of Depression, Anxiety and Stress (DASS-42): Non-clinical sample. Razavi International Journal of Medicine. 2017; 5(4):e12021. https://journal.razavihospital.ir/article_113538.html
15. Savareh BA, Bashiri A, Hatef MM, Hatef B. Prediction of salivary cortisol level by electroencephalography features. Biomedizinische Technik. Biomedical Engineering. 2020; 66(3):275-84. [DOI:10.1515/bmt-2020-0005] [PMID]
16. Van der Elst W, Van Boxtel MP, Van Breukelen GJ, Jolles J. The Stroop color-word test: Influence of age, sex, and education; and normative data for a large sample across the adult age range. Assessment. 2006; 13(1):62-79. [DOI:10.1177/1073191105283427] [PMID]
17. Hu L, Zhang Z. EEG signal processing and feature extraction. Berlin: Springer; 2019. [DOI:10.1007/978-981-13-9113-2]
18. Pincus SM, Gladstone IM, Ehrenkranz RA. A regularity statistic for medical data analysis. Journal of Clinical Monitoring. 1991; 7(4):335-45. [DOI:10.1007/BF01619355] [PMID]
19. Perfetto JC, Ruiz A, Attellis CD. Detrended Fluctuation Analysis (DFA) and R-R interval variability: A new linear segmentation algorithm. Paper presented at: Computers in Cardiology Conference. 17-20 September 2006; Valencia, Spain. https://ieeexplore.ieee.org/abstract/document/4511930
20. Riddle J, Hwang K, Cellier D, Dhanani S, D’Esposito M. Causal evidence for the role of neuronal oscillations in top-down and bottom-up attention. Journal of Cognitive Neuroscience. 2019; 31(5):768-79. [DOI:10.1162/jocn_a_01376] [PMID] [PMCID]
21. Yang K, Tong L, Shu J, Zhuang N, Yan B, Zeng Y. High gamma band EEG closely related to emotion: Evidence from functional network. Frontiers in Human Neuroscience. 2020; 14:89. [DOI:10.3389/fnhum.2020.00089] [PMID] [PMCID]
22. Iglesias-Parro S, Soriano MF, Ibáñez-Molina AJ. Fractals in affective and anxiety disorders. In: Di Ieva A, editor. The fractal geometry of the brain. New York: Springer; 2016. p. 471-83. [DOI:10.1007/978-1-4939-3995-4_29]
23. Liu Y, Yin H, Ma H, Yu X, Liu G, Guo L, et al. The salivary-α-amylase level after stroop test in anxious patients can predict the severity of anxiety. Neuroscience Letters. 2020; 715:134613. [DOI:10.1016/j.neulet.2019.134613] [PMID]
24. Ghahvehchi-Hosseini F, Manshadi E, Mohammadi A, Pirzad Jahromi G, Hatef B. [Evaluation of the persistence effect acute social stress test on the alpha band power (Persian)]. Journal of Military Medicine. 2018; 20(5):509-18. http://militarymedj.ir/article-1-2009-en.html
25. Lotfan S, Shahyad S, Khosrowabadi R, Mohammadi A, Hatef B. Support vector machine classification of brain states exposed to social stress test using EEG-based brain network measures. Biocybernetics and Biomedical Engineering. 2019; 39(1):199-213. [DOI:10.1016/j.bbe.2018.10.008]
26. Rezvani Z, Khosrowabadi R, Seyedebrahimi A, Meftahi GH, Hatef B. Alteration of brain functional network and cortisol level during induction and release of stress: An EEG study in young male adults. Basic and Clinical Neuroscience. 2020; 1-20. [DOI:10.32598/bcn.2021.2525.1]
27. Hanslmayr S, Pastötter B, Bäuml KH, Gruber S, Wimber M, Klimesch W. The electrophysiological dynamics of interference during the Stroop task. Journal of Cognitive Neuroscience. 2008; 20(2):215-25. [DOI:10.1162/jocn.2008.20020] [PMID]
28. Parris BA, Wadsley MG, Hasshim N, Benattayallah A, Augustinova M, Ferrand L. An fMRI study of response and semantic conflict in the Stroop task. Frontiers in Psychology. 2019; 10:2426. [DOI:10.3389/fpsyg.2019.02426] [PMID] [PMCID]
29. Song Y, Hakoda Y. An fMRI study of the functional mechanisms of Stroop/reverse-Stroop effects. Behavioral Brain Research. 2015; 290:187-96. [DOI:10.1016/j.bbr.2015.04.0
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| Issue | Vol 16 No 2 (2022) | |
| Section | Research Article(s) | |
| DOI | https://doi.org/10.18502/jmr.v16i2.9300 | |
| Keywords | ||
| Stroop color and word test Electroencephalograpy (EEG) Nonlinear Artificial neural network model | ||
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How to Cite
1.
Sobhani V, Rezvani Z, Meftahi GH, Ghahvehchi-Hosseini F, Hatef B. The Linear and Nonlinear Indices of Electroencephalography Change in the Stroop Color and Word Test. jmr. 2022;16(2):137-146.
