MVCA-Net: Multi-View Convolution Attention Network for measuring EEG rhythms representing Anxiety

Anxiety can significantly impact individuals' daily lives, and can manifest at varying levels from mild to severe. Traditionally, psychologists and psychiatrists assess anxiety primarily through self-report questionnaires. However, advances in computer-aided technologies and neuroimaging techniques offer promising tools to enhance diagnostic accuracy. In this study, we propose a novel deep learning model designed to extract frequency-based features from electroencephalogram (EEG) signals which provide insights into the neural patterns associated with anxiety. Our model consists of a convolutional neural network (CNN), a multi-head attention transformer, and an attention module to effectively capture EEG features distinguishing normal and anxious states. We validated our approach using a publicly available EEG dataset called DASPS, collected from 23 participants, where self-reported anxiety levels were categorized into normal and anxious conditions. The anxious condition was further subdivided into four levels of anxiety based on its severity. The proposed model achieved classification 82.94% accuracy for binary classification (normal vs. anxious) and 74.05% average accuracy for multi-class classification (normal, mild, moderate, and severe anxiety). These results highlight the effectiveness of our approach in leveraging EEG-based frequency features for anxiety assessment across different levels of severity.