Assessing target optical camouflage effects using brain functional networks: A feasibility study

Abstract

Brain functional networks model the brain's ability to exchange information across different regions, aiding in the understanding of the cognitive process of human visual attention during target searching, thereby contributing to the advancement of camouflage evaluation. In this study, images with various camouflage effects were presented to observers to generate electroencephalography (EEG) signals, which were then used to construct a brain functional network. The topological parameters of the network were subsequently extracted and input into a machine learning model for training. The results indicate that most of the classifiers achieved accuracy rates exceeding 70%. Specifically, the Logistic algorithm achieved an accuracy of 81.67%. Therefore, it is possible to predict target camouflage effectiveness with high accuracy without the need to calculate discovery probability. The proposed method fully considers the aspects of human visual and cognitive processes, overcomes the subjectivity of human interpretation, and achieves stable and reliable accuracy.