A real-time electroencephalogram (EEG) based individual identification interface for mobile security in ubiquitous environment

With the booms of mobile communication, especially mobile smart phone, technologies to identify individuals for mobile security calls for some more strict requirements in user-friendly, real-time and ubiquitous aspects. In addition to traditional approaches (for example, password check), some advanced biometric methodologies have been applied in practice, such as fingerprint and iris based solutions; however, these solutions generally lack a true ubiquitous nature for mobile security. In this paper, we present a real time EEG based individual identification interface to support ubiquitous applications. The EEG signals are collected through a monopolar single channel in real time via a mobile EEG device. An experiment involving about 20 subjects has been conducted to evaluate the interface. The experiment comprises three types of tests: accuracy test, time dimension test and capacity dimension test. The results of these experiments demonstrate that our approach is highly suitable to the demands of mobile security in ubiquitous environment. In addition, we integrate this interface into scenarios of ubiquitous application - Online Predictive Tools for Intervention in Mental Illness (OPTIMI).