Deep Convolutional Neural Network for the Detection of Psychological Stress Using Multiple Criteria for Feature Selection Determined Based on the Confidence Value of a Paired t-test
Keywords:
Psychological stress, Deep convolutional neural network, EEG signals, Stress identification, Neuropsychological disordersAbstract
This study looks at how stress impacts how people function each day and its connection to different brain health issues. It suggests the human brain plays a central role in responding to stressful things, making it important to study. The researchers used a deep neural network designed to identify stress by analyzing raw EEG signals from people who were stressed during a made-up math test simulation. This method uses something called the Montreal imaging stress task to cause stress in a controlled setting. The steps involve extracting EEG features, selecting important features using a test and four rules, and classifying using a deep neural network. The study finds the best results using rules 1, 2, and 3 instead of rule 4, with changes in power from the AF7 part of the brain being most accurate. By using objective measures linked to how the brain responds to stress, this research provides a promising way to understand and possibly reduce the bad effects of long-term stress. Using a method like this could give useful insights into stress management strategies, ultimately helping address the growing social problem of brain health issues tied to psychological stress.
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