A Novel Stress State Assessment Method for College Students Based on EEG

Abstract

Stress is an unavoidable problem for today’s college students. Stress can arouse strong personal emotional and behavioral responses. Compared with other groups of the same age, college students have a special way of life and living environment. They have complex interpersonal relationships and relatively weak social support systems. At the same time, they also face fierce competition in both academic and employment. However, they lack the skills to deal with the crisis and are reluctant to ask others for help, which leads to a simultaneous increase in mental stress. The pressure on college students mainly comes from study, family, social, employment, society, and economy. When students face multiple pressures from family, school, society, etc., some students are prone to some psychological problems due to their own personality or external environment and other reasons. Therefore, regular assessment of students’ stress status is an important means to prevent college students’ psychological problems. Considering that in real life, the number of students whose pressure is within the tolerable range is the majority, while the number of students who are under too much pressure is a minority. Therefore, the actual dataset to be identified belongs to a kind of imbalanced data. In this study, an improved extreme learning machine (IELM) is used to improve the performance of the recognition model as much as possible. IELM takes the idea of label weighting as the starting point, introduces the AdaBoost algorithm, and combines its weight distribution with the label weighted extreme learning machine (ELM). During the weight update process, the advantage of the imbalanced nature of multi-label datasets is taken. IELM was used to classify EEG data to determine the stress level of college students. The experimental results demonstrate that the algorithm used in this study has excellent classification performance and can accurately assess students’ stress levels. The accurate assessment of stress has provided a solid foundation for the development of students’ mental health and has significant practical implications.

Wearable Biosensor Technology in Education: A Systematic Review

“…New developments have not only occurred in hardware, but new software and processing techniques have also emerged. In 2022 [ 48 ], a study found better classification results from EEG data as a predictor of student stress through the use of an improved extreme learning machine model. A useful approach for EEG processing uses traditional SVMs whose features were extracted through empirical mode decomposition to obtain a higher classification accuracy in predicting student interest [ 97 ].…”

“…Meanwhile, an increase in mental fatigue was discovered to increase on 8 h school days (or longer), and it was identified as a factor of high concern in high-school education [ 64 ]. Some studies also developed algorithms for emotion recognition in teachers [ 46 ] and to evaluate psychological stress in students [ 48 , 68 ].…”

Wearable Biosensor Technology in Education: A Systematic Review

“…Meanwhile, the increase of mental fatigue was discovered to increase on 8-hour school days (or longer), and it was identified as a factor of high concern in high school education [66]. Some studies also developed algorithms for emotion recognition in teachers [46], and to evaluate psychological stress in students [48,70].…”

“…New developments have not only occurred with hardware, but new software and processing techniques have emerged. In 2022 [48], a study found better classification results from EEG data as a predictor of student stress through the use of an improved Extreme Learning Machine model. A useful approach for EEG processing uses traditional SVMs whose features were extracted through Empirical Mode Decomposition to obtain a higher classification accuracy to predict student interest [100].…”

Neutrosophic speech recognition Algorithm for speech under stress by Machine learning

“…has good classification performance in this study and is able to gauge the stress levels of kids with accuracy. The growth of students' mental health has a strong foundation thanks to the precise measurement of stress, which also has important practical ramifications [21]. This research explores the concept of the intervention effect of physical activity on college students using an integrated evaluation-based algorithm.…”