Assessing Neural Patterns of Anxiety Using Deep Learning: An EEG Study

Anxiety can have a profound effect on our lives. People may experience different levels of anxiety ranging from mild to severe, and psychologists and psychiatrists mainly rely on self-report questionnaires to measure this. However, new computer-aided technologies and neuroimaging techniques could significantly help them to verify their diagnosis. In this paper, a novel deep learning model is designed to precisely screen electroencephalogram (EEG) signals to characterise the neural patterns associated with anxiety. Our deep learning model integrates a convolutional neural network, an attention module and a recurrent neural network to effectively estimate the EEG features signifying normal and anxious emotions. In order to improve the performance of the model, we adopted a data transformation approach to generate a spatio-temporal representation of EEG data. We evaluated the performance of our proposed model using a publicly available EEG data set acquired from 23 subjects who reported feeling normal or anxious. Anxiety was further categorised into four sub-groups based on the level of anxiety. The model achieved a classification accuracy of 94.24% and 92.58% for binary (i.e normal and anxious) and multi-class (i.e normal, light, moderate and severe anxiety) scenarios, respectively. The obtained results indicated the success of our proposed model in learning EEG patterns across various levels of anxiety. Additionally, comparing the obtained results with previously published studies demonstrated considerable superiority of our method.