Evaluation of Anxiety State Discrimination by Recurrent Neural Network using the Connectivity of Brain Function Network by EEG

Abstract

This study examined the differences in functional brain network over time between different anxiety states and evaluated their usefulness in neural networks (NN). Seventeen young adults with high-anxiety and 13 young adults with low-anxiety were examined. The subjects were given three stimulations: resting, pleasant, and unpleasant stimuli, and Electroencephalogram (EEG) was measured immediately after the stimuli. EEG was analyzed for the alpha band using coherence analysis and graph theory. We evaluated the classification accuracy of anxiety states by NN and recurrent neural networks (RNN). The results showed the information processing process and structure of the brain functional network to emotional stimuli differed over time depending on the anxiety state. The time series data of coherence and graph theoretical indicator by EEG would be considered to be useful for discriminating anxiety states using RNN.