David (Yi) Wang, Department of Computer Science
EEG-based Anxious Personality Prediction and Potential Biomarker Visualization using Convolutional Neural Networks
In recent years, it has been increasingly acknowledged that anxiety disorders, which seriously affect people's quality of life, are a leading source of mental illness. Clinical anxiety disorders are hard to treat since the diagnostic method is questionnaire-based (symptom-based), which means it cannot distinguish specific biological causes of specific disorders. Since non-invasive low-cost scalp electroencephalogram (EEG) recordings can assess anxiety-related brain activity, it is a possible medium for studying the biological causes of anxiety. However, manual searching of these features is laborious, time-consuming, and error-prone. It is essential to design an automatic EEG feature extraction scheme to identify potential anxiety biomarkers.
In this study, two EEG-based Convolutional Neural Network (CNN) architectures have been proposed to predict anxious personality and visualize potential anxiety biomarkers. Intuitively, several questions have been asked.
Based on these questions, firstly, we will explore a two-dimensional Conflict-focused CNN (2-D CNN) followed by a generalized three-dimensional CNN (3-D CNN). Then, we will open the 'Blackbox' of the models and find out the decision-making components in the input space. Moreover, we will rethink the triple-blind debugging situation and discuss the Validation-Application-Exploration solution.
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