Effectiveness of dominance for Anxiety Vs Anger detection

Anxiety and anger are major indicators of psychological distress and wellbeing that require careful assessment. The identification of distinctive and overlapping features of anxiety and anger remains an important scientific problem. This paper introduces an architecture as a proof-of-concept for anxiety and anger detection and regulation in smart health environments. The aim of the proposal is to detect the subjects emotional state based on the brain activity analysis. The performance of the proposed approach was evaluated using the DEAP dataset. We reached a rate of 67.72% in classifying angry and anxious states using the SAE classifier.