BrainPrint: EEG biometric identification based on analyzing brain connectivity graphs. (September 2020)

• Record Type: Journal Article
• Authors: Wang, Min; Hu, Jiankun; Abbass, Hussein A.
• Is Part Of: Pattern recognition. Volume 105(2020:Sep.)
• Publication Date: 2020-09
• Subjects: EEG biometrics -- Brain functional connectivity -- Person identification; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107381
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: The topological features of brain connectivity graphs can be effectively used for EEG biometric identification. Seven connectivity metrics including a new one defined on the algorithmic complexity of signals, and twelve graph features are evaluated for network estimation and feature extraction. The study also analyzes the impact of EEG frequency bands and regions on biometric recognition performance and discusses the intra-subject variation issue of EEG biometrics. Abstract: Research on brain biometrics using electroencephalographic (EEG) signals has received increasing attentions in recent years. In particular, it has been recognized that the brain functional connectivity reflects individual variability. However, many questions need to be answered before we can properly use distinctive characteristics of brain connectivity for biometric applications. This paper proposes a graph-based method for EEG biometric identification. It consists of a network estimation module to generate brain connectivity networks and a graph analysis module to generate topological features based on brain networks. Specifically, we investigate seven different connectivity metrics for the network estimation module, each of which is characterized by a certain signal interaction mechanism, defining a peculiar subjective brain network. A new connectivity metric is proposed based on the algorithmic complexity of EEG signals from a information-theoretic perspective. Meanwhile, six nodal features and six global features are proposed and studied for the graph analysis module. A comprehensive evaluation is carried out to assess the impact of different connectivity metrics, graph features, and EEG frequency bands on biometric identification performance. The results demonstrate that the graph-based method proposed in this study is effective in improving the recognition rate and inter-state stability of EEG-based biometric identification systems. Our findings about the network patterns and graph features bring a further understanding of distinctiveness of humans' EEG functional connectivity and provide useful guidance for the design of graph-based EEG biometric systems.