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Therefore, researchers have committed to discover new internal biometric traits that cannot be remotely obtained and easily forged.
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However, most of these traits are easy to steal and forge given their exposure to the external world. Traditional biometric traits, such as faces, fingerprints, voiceprints, and irises, have a high degree of discrimination and are widely used. Identity authentication based on biometric traits has recently become a hot issue because of their accuracy and portability. Authentication through only accounts and passwords cannot guarantee security in important places. The rapid progress of information technology brings convenience to people but also poses great challenges to identity security. Identity authentication is an essential safety precaution in our daily lives, national security, public security, e-commerce, and other important areas. ConclusionsĮxperimental results reveal that the proposed paradigm and methods are effective for EEG-based identity authentication. Using the classification models generated from the previous sessions, a mean false rejected rate of 7.27% has been achieved, which demonstrates the robustness of our paradigm. Furthermore, in order to estimate the repeatability and stability of our paradigm, another data acquisition session is conducted for each user. The mean authentication accuracy values for the two scenarios were 91.31 and 91.61%, with 6 s time cost, which illustrated the precision and real-time capability of the system. Fifteen users and thirty imposters participated in the experiment. Two different imposter scenarios were designed to test system security, which demonstrate the capability of anti-deception. Channel selection was applied to select specific channels for each user to enhance system portability and improve discriminability between users and imposters. In contrast to previous studies that extracted EEG features from rest state or motor imagery, the designed paradigm could obtain a distinct and stable biometric trait with a lower time cost.
Eeg stands for serial#
In order to enhance its applicability in identity authentication, a novel EEG-based identity authentication method is proposed based on self- or non-self-face rapid serial visual presentation. The electroencephalogram (EEG) signal represents a subject’s specific brain activity patterns and is considered as an ideal biometric given its superior invisibility, non-clonality, and non-coercion.