EVALUATION of the RELATIONSHIP BETWEEN EEG BAND POWERS and COGNITIVE TASKS

Abstract

To examine our brain's responses to different cognitive activities, the brain signals of 30 volunteers were evaluated in terms of verbal memory, visual memory, verbal attention, visual attention and mental processing speed cognitive activities by using Electroencephalogram (EEG) signals. The correlation between cognitive activities and EEG signals was analyzed by examining the EEG signals recorded during the resting state of the volunteers and during five different cognitive activities (Öktem Verbal memory test, Wechsler Memory Scale (WMS) visual memory subtest, Digit span test, Corsi block test and Stroop test). The spectral features of four EEG subbands (delta, theta, beta and alpha) were extracted from the EEG signals at rest and from the EEG signals during each cognitive activity using the Welch method. When the extracted features were evaluated statistically using the ANOVA test, it was observed that there were changes in the EEG bands of the volunteers who passed from the resting state to the test state. It was observed that the relative beta values of the volunteers' EEG signals decreased in the Öktem verbal memory processes test and Digit span test, while the relative alpha values decreased during the visual memory, Corsi block and Stroop tests.

Keywords

• ANOVA
• Cognitive tasks
• EEG band power
• Relative alpha
• Relative beta.

References

1. [1] Berger, H., (1929), “Über das elektroenkephalogramm des menschen,” Arch. Psychiatr. Nervenkr., vol. 87, no. 1, pp. 527–570.
2. [2] Eroğlu, K.,( 2020), Duygusal Görüntülerin Lüminans Düzeylerinin EEG Üzerindeki Etkisi, Doktora Tezi, Karadeniz Teknik Üniversitesi Fen Bilimleri Enstitüsü, Trabzon, 204s.
3. [3] Amzica, F., and Steriade, M., (1998), “Electrophysiological correlates of sleep delta waves,”
4. [4] Electroencephalogr. Clin. Neurophysiol., vol. 107, no. 2, pp. 69–83.
5. [5] Schacter, D. L.,(1977), “EEG theta waves and psychological phenomena: A review and analysis,” Biol. Psychol., vol. 5, no. 1, pp. 47–82.
6. [6] Toscani, M., Marzi, T., Righi, S., Viggiano, M. P., and Baldassi, S., (2010), “Alpha waves: a neural signature of visual suppression,” Exp. brain Res., vol. 207, no. 3, pp. 213–219.
7. [7] Dustman, R. E., Boswell, R. S., and Porter, P. B., (1962), “Beta brain waves as an index of alertness,” Science (80-. )., vol. 137, no. 3529, pp. 533–534.
8. [8] Shigihara, Y., Tanaka, M., Ishii, A., Tajima, S., Kanai, E., Funakura, M., and Watanabe, Y., (2013), “Two different types of mental fatigue produce different styles of task performance,” Neurol. Psychiatry Brain Res., vol. 19, no. 1, pp. 5–11, 2013.

9. [9] Jiao, X., Bai, J., Chen, S., and Li, Q., (2012), “Research on mental fatigue based on entropy changes in space environment,” in 2012 IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS) Proceedings, pp. 74–77.
10. [10] He, D., Donmez, B., Liu, C. C., and Plataniotis, K. N., (2019), “High cognitive load assessment in drivers through wireless electroencephalography and the validation of a modified N-back task,” IEEE Trans. Human-Machine Syst., vol. 49, no. 4, pp. 362–371.
11. [11] Guevara, M. A., Paniagua, E. I., Gonzalez, M. H., Carrillo, I. K. S., Sepulveda, M. L. A., Orozco, J.C.H., and Gutierrez, C. A., (2018), “EEG activity during the spatial span task in young men: Differences between short-term and working memory,” Brain Res., vol. 1683, pp. 86–94.
12. [12] Corsi, P. M., (1972), Human memory and the medial temporal region of the brain (Doctoral dissertation, McGill University).
13. [13] Harmony, T., Fernandez, T., Silva, J., Bosch, J., Valdes, P., Fernandez-Bouzas, A., Galan, L., Aubert, E., and Rodriguez, D., (1999), “Do specific EEG frequencies indicate different processes during mental calculation?,” Neurosci. Lett., vol. 266, no. 1, pp. 25–28.
14. [14] Moeller, K., Wood, G., Doppelmayr, M., and Nuerk, H.-C., (2010), “Oscillatory EEG correlates of an implicit activation of multiplication facts in the number bisection task,” Brain Res., vol. 1320, pp. 85–94.
15. [15] Gärtner, M., Grimm, S., and Bajbouj, M., (2015), “Frontal midline theta oscillations during mental arithmetic: effects of stress,” Front. Behav. Neurosci., p. 96.
16. [16] Spüler, M., Walter, C., Rosenstiel, W., Gerjets, P., Moeller, K., and Klein, E., (2016), “EEG-based prediction of cognitive workload induced by arithmetic: a step towards online adaptation in numerical learning,” Zdm, vol. 48, no. 3, pp. 267–278.
17. [17] Chin, Z. Y., Zhang, X., Wang, C., and Ang, K. K., (2018), “EEG-based discrimination of different cognitive workload levels from mental arithmetic,” in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1984–1987.
18. [18] Hanslmayr, S., Volberg, G., Wimber, M., Raabe, M., Greenlee, M. W., and Bäuml, K.-H. T., (2011), “The relationship between brain oscillations and BOLD signal during memory formation: a combined EEG–fMRI study,” J. Neurosci., vol. 31, no. 44, pp. 15674–15680.
19. [19] Scholz, S., Schneider, S. L., and Rose, M., (2017), “Differential effects of ongoing EEG beta and theta power on memory formation,” PLoS One, vol. 12, no. 2, p. e0171913.
20. [20] León-Cabrera, P., Piai, V., Morís, J., and Rodríguez-Fornells, A., (2022), “Alpha power decreases associated with prediction in written and spoken sentence comprehension,” Neuropsychologia, p. 108286.
21. [21] Prystauka, Y., and Lewis, A. G., (2019), “The power of neural oscillations to inform sentence comprehension: A linguistic perspective,” Lang. Linguist. Compass, vol. 13, no. 9, p. e12347.
22. [22] Molinaro, N., Monsalve, I. F., and Lizarazu, M., (2016), “Is there a common oscillatory brain mechanism for producing and predicting language?,” Lang. Cogn. Neurosci., vol. 31, no. 1, pp. 145–158.
23. [23] Öktem, Ö., (1994), “Nöropsikolojik testler ve nöropsikolojik değerlendirme,” Türk Psikol. Derg., vol. 9, no. 33, pp. 33–44
24. [24] Tanör, Ö. Ö., (2011) “Öktem sözel bellek süreçleri testi.(Öktem-SBST) el kitabı.” Türk Psikologlar Derneği.
25. [25] Wechsler, D. (1987). The Wechsler Memory Scale—Revised. Advances in Psychological Assessment. Advances in Psychological Assessment, içinde (s. 65-99), Boston, MA.
26. [26] Karakaş, S., and Yalın, A., (1995), “Görsel işitsel sayı dizileri testi B formunun 13-54 yaş grubu üzerindeki standardizasyon çalışması,” Türk Psikol. Derg., vol. 10, no. 34, pp. 20–31.
27. [27] Osmanlıoğlu, U. M. ve Özgüzel, M. (1985). Hafıza bozukluğu gösteren çeşitli tanı gruplarındaki hastaların Wechsler Hafıza Ölçeği ile tetkiki. XXI. Psikiyatri ve Nörolojik Bilimler Kongresi Kitabı, içinde. Adana: Çukurova Üniversitesi Yayını.
28. [28] Jensen, A. R., and Rohwer Jr, W. D., (1966), “The Stroop color-word test: a review,” Acta Psychol. (Amst)., vol. 25, pp. 36–93.
29. [29] Karakaş, S., Erdoğan, E., Soysal, A. Ş., Ulusoy, T., Ulusoy, İ., and Alkan, S., (1999), “Stroop Testi TBAG Formu : Türk Kültürüne Standardizasyon Çalışmaları ,” Türk Psikiyatr. Derg., vol. 2, pp. 75–88.
30. [30] Stone, J. V, (2002), “Independent component analysis: an introduction,” Trends Cogn. Sci., vol. 6, no. 2, pp. 59–64.
31. [31] Vigârio, R., Särelä, J., Jousmäki, V., Hämäläinen, M., and Oja, E., (2000), “Independent component approach to the analysis of EEG and MEG recordings,” IEEE Trans. Biomed. Eng., vol. 47, no. 5, pp. 589–593.
32. [32] Jayant, H. K., Rana, K. P. S., Kumar, V., Nair, S. S., and Mishra, P., (2016), “Efficient IIR notch filter design using Minimax optimization for 50Hz noise suppression in ECG,” Proc. 2015 Int. Conf. Signal Process. Comput. Control. ISPCC 2015, pp. 290–295.
33. [33] Welch, P. D., (1967), “The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodograms,” IEEE Trans. Audio Electroacoust., vol. 15, no. 2, pp. 70–73.
34. [34] Villwock S., and Pacas, M., (2008), “Application of the Welch-method for the identification of two-and three-mass-systems,” IEEE Trans. Ind. Electron., vol. 55, no. 1, pp. 457–466.
35. [35] Dimitrakopoulos, G. N., Kakkos, I., Dai, Z., Lim, J., deSouza, J.J., Bezerianos, and A.,Sun, Y., (2017) “Task-independent mental workload classification based upon common multiband EEG cortical connectivity,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 25, no. 11, pp. 1940–1949, 2017.
36. [36] Fink, A., Grabner, R. H., Neuper, C., and Neubauer, A. C., (2005), “EEG alpha band dissociation with increasing task demands,” Cogn. brain Res., vol. 24, no. 2, pp. 252–259.
37. [37] Stipacek, A., Grabner, R. H., Neuper, C., Fink, A., and Neubauer, A. C., (2003), “Sensitivity of human EEG alpha band desynchronization to different working memory components and increasing levels of memory load,” Neurosci. Lett., vol. 353, no. 3, pp. 193–196.
38. [38] Foxe, J. J., and Snyder, A. C., (2011), “The role of alpha-band brain oscillations as a sensory suppression mechanism during selective attention,” Front. Psychol., vol. 2, p. 154.
39. [39] Payne, L., Guillory, S., and Sekuler, R., (2013) “Attention-modulated alpha-band oscillations protect against intrusion of irrelevant information,” J. Cogn. Neurosci., vol. 25, no. 9, pp. 1463–1476.