EEG-Based Anxious States Classification Using Affective BCI-Based Closed Neurofeedback System

Anxiety disorder is one of the psychiatric disorders that involves extreme fear or worry, which can change the balance of chemicals in the brain. To the best of our knowledge, the evaluation of anxiety state is still based on some subjective questionnaires and there is no objective standard assessment yet. Unlike other methods, our approach focuses on study the neural changes to identify and classify the anxiety state using electroencephalography (EEG) signals.

We designed a closed neurofeedback experiment that contains three experimental stages to adjust subjects' mental state. The EEG resting state signal was recorded from thirty-four subjects in the first and third stages while EEG-based mindfulness recording was recorded in the second stage. At the end of each stage, the subjects were asked to fill a Visual Analogue Scale (VAS). According to their VAS score, the subjects were classified into three groups: non-anxiety, moderate or severe anxiety groups.

After processing the EEG data of each group, support vector machine (SVM) classifiers were able to classify and identify two mental states (non-anxiety and anxiety) using the Power Spectral Density (PSD) as patterns. The highest classification accuracies using Gaussian kernel function and polynomial kernel function are 92.48 ±   1.20% and 88.60   ±   1.32%, respectively. The highest average of the classification accuracies for healthy subjects is 95.31 ±   1.97% and for anxiety subjects is 87.18 ±   3.51%.

The results suggest that our proposed EEG neurofeedback-based classification approach is efficient for developing affective BCI system for detection and evaluation of anxiety disorder states.