Analisis Perbedaan Pola Sinyal EEG Berdasarkan Jenis Kelamin Yang Berbeda Saat Numerical Stroop Task

https://doi.org/10.22146/ijeis.34383

Riswandha Latu Dimas(1*), Catur Atmaji(2)

(1) Prodi Elektronika dan Instrumentasi, DIKE, FMIPA, UGM, Yogyakarta, Indonesia
(2) Departemen Ilmu Komputer dan Elektronika, Program Studi Elektronika dan Instrumentasi
(*) Corresponding Author

Abstract


Cognitive process show how brain work from stimulus reception until stimuls reaction. With electroencephalogram (EEG) device, cognate process can be observerd in brain signal or EEG signal form. In cognitive process different kind of stimulus could affect generated brain signal. Also, given interference in cognitive prcess could affect brain signal. In this research, conducted observation whether gender difference has effect in cognitive process. Numerical stroop task with three kinds of conditions (congruence, incongruence, and neutral) are used as reference in signal observation process which is generated when the cognitive process in difference genders are done. The resulting EEG signal then conducted three kinds of analysis that is ERP analysis, reaction time, and energy analysis. The result of ERP analysis show both subject class have difference in response time that indicated with P3 peak time. On average, respons time in female (kongruent = 623,34 ms; inkongruent = 645,18 ms ; neutral = 614,91 ms)subject class is faster than male (kongruent = 709,67 ms; inkongruent = 745,00 ms; neutral =715,37 ms) subject class. Energy analysis show when numerical stroop task takes place, left side of the brain (51,36%) and cetral side of the brain (50,65%) more dominant than others parts of the brain.


Keywords


EEG; ERP; Stroop Task; Numerical Stroop Task

Full Text:

PDF


References

[1] A. F. Jadidi, B. S. Zargar, and M. H. Moradi, “Categorizing visual objects; Using ERP components,” 2016 23rd Iran. Conf. Biomed. Eng. 2016 1st Int. Iran. Conf. Biomed. Eng. ICBME 2016, no. November, pp. 23–25, 2017.

[2] S. Sanei and J. A. Chambers, EEG Signal Processing, vol. 1. West Sussex: John Wiley & Sons, Ltd, 2007.

[3] C. Atmaji and Z. Y. Perwira, “Pengaruh Latar Belakang Warna pada Objek Gambar terhadap Hasil Ekstraksi Sinyal EEG,” Indones. J. Electron. Instrum. Syst., vol. 7, no. 2, pp. 161–172, 2017 [Online]. Available: https://jurnal.ugm.ac.id/ijeis/article/view/22893

[4] M. E. Alam and B. Samanta, “Empirical Mode Decomposition of Eeg Signals for Synchronisation,” Proc. SoutheastCon 2017, pp. 1–2, 2017 [Online]. Available: http://ieeexplore.ieee.org/document/7925341/

[5] A. Y. Tychkov et al., “EEG Analysis Based on the Empirical Mode Decomposition for Detection of Mental Activity,” 2017 IVth Int. Conf. Eng. Telecommun., no. 17, pp. 130–134, 2017 [Online]. Available: http://ieeexplore.ieee.org/document/8241271/

[6] S. Kaneta, I. Wakabayashi, and T. Kawahara, “Feasibility of BMI improvement applying a Stroop effect,” 2016 18th Int. Conf. Adv. Commun. Technol., pp. 681–684, 2016 [Online]. Available: http://ieeexplore.ieee.org/document/7423518/

[7] E. Beldzik, A. Domagalik, W. Froncisz, and T. Marek, “Dissociating EEG sources linked to stimulus and response evaluation in numerical Stroop task using Independent Component Analysis,” Clin. Neurophysiol., vol. 126, no. 5, pp. 914–926, 2015 [Online]. Available: http://dx.doi.org/10.1016/j.clinph.2014.08.009

[8] A. Chuderski, M. Senderecka, P. Kalamala, B. Kroczek, and M. Ociepka, “ERP correlates of the conflict level in the multi-response Stroop task,” Brain Res., vol. 1650, pp. 93–102, 2016 [Online]. Available: https://doi.org/10.1016/j.brainres.2016.08.041

[9] E. Roivainen, “Gender differences in processing speed: A review of recent research,” Learn. Individ. Differ., vol. 21, no. 2, pp. 145–149, 2011 [Online]. Available: http://dx.doi.org/10.1016/j.lindif.2010.11.021

[10] N. Ueda, K. Watanabe, and K. Tanaka, “Gender differences in visuomotor sequence learning,” 2016 8th Int. Conf. Knowl. Smart Technol. KST 2016, pp. 271–274, 2016 [Online]. Available: http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=7440507

[11] M. Bilalpur, S. M. Kia, T.-S. Chua, and R. Subramanian, “Discovering Gender Differences in Facial Emotion Recognition via Implicit Behavioral Cues,” Proc. Seventh Int. Conf. Affect. Comput. Intell. Interact., pp. 119–124, 2017 [Online]. Available: http://arxiv.org/abs/1708.08729

[12] S. Bulárka and A. Gontean, “Brain-Computer Interface Review,” 2016 12th IEEE Int. Symp. Electron. Telecommun., 2016 [Online]. Available: http://ieeexplore.ieee.org/document/7781096/

[13] S. J. Luck, An Introduction to the Event-Related Potential Technique, Second Edi. The MIT Press, 2014.

[14] A. Arasteh, M. H. Moradi, and A. Janghorbani, “A Novel Method Based on Empirical Mode Decomposition for P300-Based Detection of Deception,” IEEE Trans. Inf. Forensics Secur., vol. 11, no. 11, pp. 2584–2593, 2016.

[15] M. C. Corballis, “Left Brain, Right Brain: Facts and Fantasies,” PLoS Biol., vol. 12, no. 1, 2014 [Online]. Available: https://doi.org/10.1371/journal.pbio.1001767



DOI: https://doi.org/10.22146/ijeis.34383

Article Metrics

Abstract views : 3224 | views : 4592

Refbacks

  • There are currently no refbacks.




Copyright (c) 2018 IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)

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



Copyright of :
IJEIS (Indonesian Journal of Electronics and Instrumentations Systems)
ISSN 2088-3714 (print); ISSN 2460-7681 (online)
is a scientific journal the results of Electronics
and Instrumentations Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijeis.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijeis



View My Stats1
View My Stats2