STRESS LEVEL AND COGNITIVE LOAD TEST OF HIGH SCHOOL STUDENTS BASED ON THE ANALYSIS OF CONSUMER-GRADE EEG SIGNAL AND NASA-TLX QUESTIONNAIRE
DOI:
https://doi.org/10.11113/jurnalteknologi.v88.24938Abstract
Brain wave data collection had been conducted on 35 students from SMAN 2 Cikarang Selatan, Indonesia, during their physics exam between August and September 2024. This research aimed to process brain wave activity experienced by students three minutes before the exam, during the 40-minute exam period, and three minutes after the exam, focusing on stress management that may affect brain activity through amplitude changes and frequency shifts using the consumer-grade EEG device Muse 2.0. Mental demand and frustration can be objectively measured using EEG through Beta/Alpha (β/α) power ratio and Theta/Beta (θ/β) power ratio. In addition to using EEG, students' cognitive load is also assessed subjectively using the NASA-TLX questionnaire. The analysis of β/α power ratio differences after and before the exam categorized subjects into five groups: Positive 2, Positive 1, Neutral, Negative 1, and Negative 2. The results indicate that a certain level of increased cognitive load during the exam led to lower test scores than neutral cognitive load conditions. A rise in β wave power during the exam provides insight into individuals striving to engage with cognitive tasks optimally. The β/α power ratio difference has a moderate positive correlation with increased mental demand, suggesting that subjects experienced excessive cognitive load during the exam. Higher mental demand is associated with an increase in the power of β frequency in the frontal lobe. Additionally, a weak positive correlation was found between the frustration indicator in NASA-TLX and the (θ/β) power ratio difference.
References
F. Saffari, K. Norouzi, L. Bruni, S. Zarei and T. Ramsoy, "Impact of Varying Levels of Mental Stress on Phase Information of EEG Signals: A study on the Frontal, Central, and Parietal Regions," Biomedical Signal Processing and Control, vol. 86, 2023.
S. Kumar, "Examination Stress and Its Effect on EEG," International Journal of Medical Science and Public Health, vol. 4, no. 11, 2015.
OECD, "PISA 2015 Results (Volume III): Students’ Well-Being," 2017. [Online].
A. Qayoom, A. Wahab, . N. Kamaruddin and Z. , "Artifacts Classification In EEG Signals Based On Temporal Average Statistics," Jurnal Teknologi, vol. 77, no. 7, pp. 73-77, 2015.
S. . Z. Mohd Tumari and . R. Sudirman, "Working Memory Impairments Imitate Age-Related Behaviors in Children using Visual Stimulation Based on Event-Related Potentials," Jurnal Teknologi, vol. 74, no. 6, pp. 55-63, 2015.
R. Katmah, F. Al-Shargie, U. Tariq, F. Babiloni, F. Al-Mughairbi and H. Al-Nashash, "A Review on Mental Stress Assessment Methods Using EEG Signals," Sensors, vol. 21, 2021.
N. Schaworonkow, "Overcoming harmonic hurdles: Genuine beta-band rhythms vs. contributions of alpha-band waveform shape," Imaging Neuroscience, vol. 1, pp. 1-8, 2023.
B. J. Grriffiths, S. D. Mayhew, K. J. Mullinger, J. Jorge, I. Charest, M. Wimber and S. Hanslmayr, "Alpha/beta power decreases track the fidelity of stimulus-specific information," elife, vol. 8, 2019.
P. Putman, B. Verkuil, E. Arias-Gracia, I. Pantazi and C. van Schie, "EEG Theta/beta Ratio as a Potential Biomarker for Attentional Control and Resilience Against Deleterious Effects of Stress on Attention," Cogn Affect Behav Neurosci, vol. 14, pp. 782-791, 2014.
F. M. Al-Shargie, M. Kiguchi, N. Badruddin, S. Dass, A. F. M. Hani and T. B. Tang, "Mental stress assessment using simultaneous measurement of EEG and fNIRS," Biomedical Optics Express, vol. 7, no. 10, pp. 3882-3898, 2016.
J. Xie, G. Xu, J. Wang, M. Li, C. Han and Y. Jia, "Effects of mental," PLoS ONE, vol. 11, 2016.
S. Puma, N. Matton, P. Paubel, E. Raufaste and R. El-Yagoubi, "Using theta and alpha band power to assess cognitive workload in multitasking environments," Int. J. Psychophysiol, vol. 123, pp. 111-120, 2018.
G. Borghini, G. Vecchiato, J. Toppi, L. Astolfi, A. Maglione and R. Isabella, "Assessment of mental fatigue during car driving by using high resolution eeg activity and neurophysiologic indices," in Annual International Conference IEEE Engineering, Medical, Biology, Social, 2012.
A. T. Kamzanova, A. M. Kustubayeva and G. Matthews, "Use of eeg workload indices for diagnostic monitoring of vigilance decrement," Hum. Factors, vol. 56, pp. 1136-1149, 2014.
M. Mazher, A. Abd Aziz, A. Malik and H. U. Amin, "An eeg-based cognitive load assessment in multimedia learning using featureextraction and partial directed coherence," IEEE Access, vol. 5, p. 14819–14829, 2017.
A. Wrobel, "Beta activity: a carrier for visual attention," Acta Neurobiol Exp, vol. 60, pp. 247-260, 2000.
S. Palva, S. Kulashekhar, M. Hämäläinen and J. M. Palva, "Localization of cortical phase and amplitude dynamics during visual working memory encoding and retention," J. Neurosci, vol. 31, pp. 5013-5025, 2011.
B. Spitzer and S. Haegens, "Beyond the Status Quo: A Role for Beta Oscillations in Endogenous Content (Re)Activation," eNeuro, vol. 4, no. 4, 2017.
S. Coelli, R. Sclocco, R. Barbieri, G. Reni, C. Zucca and A. M. Bianchi, "EEG-based index for engagement level monitoring during sustained attention," in 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, 2015.
I. Kakkos, G. N. Dimitrakopoulos, L. Gao, Y. Zhang, P. Qi and G. K. Matsopoulos, "Mental workload drives different reorganizations of functional cortical connectivity between 2d and 3d simulated flight experiments," EEE Trans. Neural Syst. Rehabil. Eng, vol. 27, p. 1704–1713, 2019.
M. H. MacLean, K. M. Arnell and K. A. Cote, "Resting eeg in alpha and beta bands predicts individual differences in attentional blink magnitude," Brain Cognitive, vol. 78, pp. 218-229, 2012.
P. Putman, J. van Peer, I. Maimari and S. van der Werff, "EEG Theta/Beta ratio in relation to fear-modulated response-inhibition, attentional control, and affective traits," Biological Psychology, vol. 83, no. 2, pp. 73-78, 2010.
B. Raufi and L. Longo, "An evaluation of the EEG Alpha to Theta and Theta to Alpha Band Ratios as Indexes of Mental Workload," Neuroinformatics, vol. 16, 2022.
J. Clutterbuck, "Muse Developer Archive," 2018. [Online]. Available: https://web.archive.org/web/20181105231756/http://developer.choosemuse.com/tools/available-data#Absolute_Band_Powers. [Accessed Juni 2024].
NASA, "Nasa Task Load Index (TLX) v. 1.0 Manual," 1986. [Online]. Available: https://ntrs.nasa.gov/api/citations/20000021488/downloads/20000021488.pdf. [Accessed Mei 2024].
T. Y. Wen and S. Armiza M.A, "Electroencephalogram (EEG) Stress Analysis on Alpha/Beta Ratio and Theta/Beta Ratio," Indonesian Journal of Electrical Engineering and Computer Science, vol. 17, pp. 175-182, 2020.
V. G. Rajendran, S. Jayalalitha and K. Adalarasu, "EEG Based Evaluation of Examination Stress and Test Anxiety Among College Students," IRBM, vol. 43, pp. 349-361, 2022.
S. Chikhi, N. Matton and S. Blanchet, "EEG Power Spectral Measures of Cognitive Workload: A Meta Analysis," Psychophysiology, vol. 59, no. e14009, 2022.
B. Vugs, M. Hendriks, J. Cuperus and L. Verhoeven, "Working Memory Performance and Executive Function Behaviors in Young Children with SLI," Research in Developmental Disabilities, vol. 35, no. 1, pp. 62-74, 2014.
M. A. Hafeez, S. Shakil and S. Jangsher, "Stress Effects on Exam Performance using EEG," in 14th International Conference on Emerging Technologies (ICET), Islamabad, Pakistan, 2018.
J. S. Kumar and P. Bhuvaneswari, "Analysis of Electroencephalography (EEG) Signals and Its Categorization - a study," Procedia Engineering, vol. 38, 2012.
G. J. Tortora and B. Derrickson, Principles of Anatomy and Physiology, 14th Edition ed., Wiley, 2015.
A. Sahroni, H. Setiawan, F. Mahananto and H. Zakaria, "Objective Stress Measurement: Studi Korelasi Parameter Saliva Amylase dan Aktivitas Gelombang Otak Menggunakan Electroencephalograph (EEG)," Transmisi, vol. 22, no. 1, 2020.
H. Kwon, J. Cho and E. Lee, "EEG Asymmetry Analysis of the Left and Right Brain Activities During Simple versus Complex Arithmetic Learning," Journal of Neurotherapy, vol. 13, pp. 109-116, 2009.
E. T. Attar, "Review of Electroencephalography Signals Approaches for Mental Stress Assessment," Neurosciences, vol. 27, no. 4, pp. 209-215, 2022.
R. F. Awanis, S. Khabibah and E. M. Imah, "Analisis Beban Kerja Kognitif Siswa SMP pada Tugas Aritmetika Mental," EDUKASIA: Jurnal Pendidikan dan Pembelajaran, vol. 4, no. 1, pp. 509-520, 2023.
P. S. Tsang and M. A. Vidulich, "Mental workload and situation awareness," in Proc. Hum. Factors Ergonom. Soc. Ann. Meeting, 2006.
P. Antonenko, F. Paas, R. Grabner and T. Van Gog, "Using electroencephalography to measure cognitive load," Educ. Psychol. Rev, vol. 22, pp. 425-438, 2010.
G. G. Knyazev, "Motivation, emotion, and their inhibitory control mirrored in brain oscillations," Neuroscience & Biobehavioral Reviews, vol. 31, no. 3, pp. 377-395, 2007.
N. Salma, B. Mai, K. Namuduri, R. Mamun, Y. Hashem, H. Takabi, N. Parde and R. Nielsen, "Using EEG Signal to Analyze IS Decision Making Cognitive Processes," Information Systems and Neuroscience, vol. 25, 2018.
J. Sanger, L. Bechtold, D. Schoofs, M. Blaszkewicz and E. Wascher, "The Influence of Acute Stress on Attention Mechanisms and Its Electrophysiological Correlates," Frontiers in Behavioral Neuroscience, vol. 8, p. 353, 2014.
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