An EEG based familiar and unfamiliar person identification and classification system using feature extraction and directed functional brain network

People are extremely proficient at recognizing familiar person, but are much worse at matching unfamiliar one. However, the neural correlation of this proposed difference in neural representations of familiar and unfamiliar identities remains unclear. New methods of EEG data analysis, functional networks and time frequency analyses, are highly recommended to advance the knowledge of those brain mechanisms. Developing an EEG based pattern recognition system could potentially be used to improve the current person recognition strategies. In this study, we designed a multi-channel EEG based pattern recognition system for person recognition. To do this, a new feature extraction method combining directed functional network analysis and signal complexity of EEGs from different brain regions was proposed, which is the main contribution of this paper. The proposed method was tested in an experiment of 20 subjects underlying visual and auditory stimuli simultaneously. The features were calculated in delta, theta, alpha and beta band respectively, then SVM and KNN classifiers were applied to these feature sets and the results showed the recognition accuracies of these four bands are relatively stable with the best accuracy of 90.58% in delta band for SVM. In addition, theta and alpha band also showed good performance for the two classifiers. It indicated delta wave is the best sub band for person perception and SVM is better than KNN in this system. This work is the first time to construct the directed functional network in person recognition study, and it demonstrated the combination of non-linear complexities and network features are efficient for EEG based expert and intelligent system for person recognition. (C) 2020 Elsevier Ltd. All rights reserved.