Advancing the neurophysiological understanding of stress, a study based on recorded Electroencephalography (EEG) data in real-world classroom

Stress has been a prevalent part of modern life, particularly during the time of the pandemic. While short-term stress may cause little harm to productivity, if left untreated for a long period of time, it could eventually lead to anxiety and depression, which significantly decrease the quality of life. Such a problem is even more severe among students. A recent survey conducted in 2020 with 15,346 graduate and professional students had shown that 32% of them screened positive for major depressive disorder. This study, which took place in a real-world classroom, aims to uncover some of the neuronal mechanisms behind stress among young adults with their recorded EEG data. Such understanding could provide the theoretical foundation for stress reduction and prevention techniques such as real-time stress detection and non-invasive neurostimulation. This thesis is structured into four chapters. In the first chapter, the author introduced the importance of the problem, the experiment design, and the dataset. In the next chapter, the author began the stress analysis by studying the power spectral density of the 30 EEG electrodes. Results showed that most theta (4-8Hz) and alpha (8-13Hz) frequency bands in the frontal, central, and right parietal regions showed statistical significance among the elevated and normal stress groups. While the power spectra information is helpful for understanding stress, it is important to remember that EEG signals are mixtures of source activities, which makes the underlying source activities and locations unknown. Thus, in chapter three, the author decomposed the EEG data into independent sources using Independent Component Analysis (ICA) and analyzed the effect of stress in terms of cortical source activities. The results showed that some sources responded to stress while others did not. One limitation of this study was that each source was analyzed individually. Thus, in the final chapter, the author focused on exploring the interactions between regions (i.e. effective connectivity) under stress. The results showed that the information inflow and outflow near the central region were statistically different between the elevated stress and normal stress groups