Key result
Novel IELM algorithm boosts EEG-based stress classification accuracy by ~10% over traditional ELM.
Why the study?
College students face multiple stressors leading to psychological problems, requiring regular stress assessment despite the challenge of dealing with imbalanced real-life stress data.
An improved extreme learning machine algorithm using EEG data can accurately classify stress levels in college students, outperforming traditional machine learning models.
IELM advances EEG-based stress classification in students; leaves open clinical utility pending prospective validation.
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.
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Liu et al. (2022) studied Psychological stress (n=90). Improved extreme learning machine (IELM) algorithm vs. Traditional extreme learning machine (ELM) and other machine learning models was evaluated on Classification accuracy of stress levels. The improved extreme learning machine (IELM) algorithm accurately assessed college students' stress levels using EEG data, improving classification accuracy by 10.25% compared to the traditional ELM model.
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