An Algorithm for Quantifying Anxiety, Stress, and Attention: Towards Objective Assessment of Mental States

Abstract

Different types of brain electrical activity investigations may provide valuable information for understanding mental states. Lack of anxiety, stress, and attention must be addressed appropriately to develop a healthy society. According to current studies, the assessment and differentiation between different mental states are still based on questionnaires, and there are still no standard digitalized tools to help diagnose. Unlike other research teams, we studied neural changes and aimed to identify and classify mental states by performing electroencephalographic (EEG) signal processing. So, we developed an algorithm for diagnosing anxiety, stress, and attention levels. The algorithm's basis is a mathematical apparatus that provides three indexes to assess attention, anxiety, and stress levels. The algorithm could contribute to many therapies as a support tool, such as Neurofeedback therapy, Mindfulness-based interventions, Biofeedback therapy, Cognitive restructuring therapy, and Cognitive behavioral therapy (CBT). In addition, our algorithm can provide valuable contributions in other related domains such as Clinical psychology, Neuropsychology, Human-computer interaction, Education, Sports Psychology, etc.