Individual Identification Using Cognitive Electroencephalographic Neurodynamics

• Published in: IEEE transactions on information forensics and security Vol. 12; no. 9; pp. 2159 - 2167
• Main Authors: Min, Byoung-Kyong, Suk, Heung-Il, Ahn, Min-Hee, Lee, Min-Ho, Lee, Seong-Whan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain; Causality; cognitive system; Decoding; Electrodes; Electroencephalography; Electrooculography; Estimation; Feasibility; Feature extraction; identification; Neurodynamics; Recognition; support vector machine; top-down processing; Visual signals; Visualization
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2017.2699944

As the brain is a unique biological system that reflects the subtle distinctions in the mental attributes of individual humans, electroencephalographic (EEG) signals have been regarded as one of the most promising and potent biometric signals for discriminating between individuals. However, existing EEG-based user-recognition methods present only a limited range of individual distinctions. In this paper, we propose a novel system of decoding cognitive EEG signals for individual identification with high accuracy. Specifically, we investigate the feasibility of our system, which can recognize an individual based on the discriminative patterns of source-level causal connectivity among brain regions, estimated from scalp-level EEG signals. The EEG signals were produced by a steady-state visual evoked potential-inducing grid-shaped top-down paradigm. This system can, in principle, use top-down cognitive features analyzed by individuals' differently characterized neurodynamic causal connectivities. In this paper, we achieved a maximal accuracy of 98.60% on average in 20 subjects, for whom we estimated causal connectivity in 16 brain regions using 5-s intervals of EEG signals. Our system shows promising initial results toward building a practical identification technology able to recognize individuals by means of brain neurodynamics.