EEG-based stress recognition amongst Universiti Sains Malaysia (USM) students
Introduction: Many students struggle with stress associated with their studies regardless of school, college, or university. Research has revealed that students who have excessive stress will have difficulty focusing on learning, which has a negative impact on academic outcomes that lead to healt...
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| Format: | Thesis |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://eprints.usm.my/54046/1/Eizan%20Azira%20Mat%20Shari-24%20pages.pdf http://eprints.usm.my/54046/ |
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| Summary: | Introduction: Many students struggle with stress associated with their studies
regardless of school, college, or university. Research has revealed that students who
have excessive stress will have difficulty focusing on learning, which has a negative
impact on academic outcomes that lead to health problems.
Purpose: This study aimed to detect stress among undergraduate and postgraduate
students from various faculties of Universiti Sains Malaysia (USM), Main Campus,
Penang.
Methodology: The electroencephalography (EEG) system was used to identify
student's brainwave patterns while exposing different stress levels. EEG was chosen
because it offers several advantages such as non -invasive data acquisition, ease of use,
low-cost preparation, and a high temporal resolution in milliseconds. Besides that, the
researcher used the Perceived Stress Scale - the self-assessment instrument, to assess
students’ stress levels. In this study, the researcher applied four Stroop Tests to induce
stress.
Results: The results showed that the alpha and beta waves were the most common
higher frequency bands among undergraduate and postgraduate students. The
researcher decided to apply the study from Priyanka (2016); therefore, the beta wave
was considered the stress detection level. Entropy and Standard Deviation were the
accurate classifiers to detect stress levels. Statistical analysis showed the mean values
for PSS10 Score undergraduate (n=24) = 21.67 and for postgraduate (n=6) = 21.17
with the p-value of .228. The p-value was greater than 0.05 (p > 0.005), therefore,
there were no significant mean differences of the perceived stress scale between
undergraduate and postgraduate students from various faculties of Universiti Sains
Malaysia (USM), Main Campus (Penang) during stress-inducing tasks. For perceived
stress scale score between gender (male and female) revealed that the mean values for
male (n=15) = 21.47 and female (n=15) = 21.67 with the p-value of .847, and the pvalue
was greater than 0.05 (p > 0.05). As a result, there were no significant differences
in perceived stress scores between males and females from various faculties of
Universiti Sains Malaysia (USM), Main Campus (Penang) during stress-inducing
tasks. The two-way repeated-measures ANOVA for duration revealed no significant
difference in the duration of the Stroop tests (F (3, 87)) = 1.860, p =.142, and for
between-group interaction showed no significant difference in the duration of the
Stroop tests between programs within the four Stroop tests (F (3,84)) = .061, p = .980.
Conclusions: It can be concluded that this study that detects the stress level among
students using an EEG system could alter the way of detection and treatment of some
severe health problems over other current practises. It provided us with a more diverse
assessment of stress conditions that might not be possible for one to express. The
combination of signal processing techniques such as Wavelet Transform and Coiflet1
with three formulas from Energy, Entropy and Standard Deviation features developed
by the time-frequency analysis of EEG signals proved to enhance accuracy. |
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