LAUSR

Biometrics is aimed at recognizing individuals based on physical, physiological, or behavioural characteristics of a human body such as fingerprint, gait, voice, iris, and gaze. Currently, the state-of-the-art methods for biometric authentication are being incorporated in various access control and personal identity management applications. While the hand-based biometrics (including fingerprint) have been the most often used technology so far, there is growing evidence that electroencephalogram (EEG) signals collected during a perception or mental task can be used for reliable person recognition. However, the domain of EEG-based biometry still faces the problems of improving the accuracy, robustness, security, privacy, and ergonomics of the EEG-based biometric systems and substantial efforts are needed towards developing efficient sets of stimuli (visual or auditory) that can be used of person identification in Brain-Computer Interface (BCI) systems and applications. There are still many challenging problems involved in improving the accuracy, efficiency, and usability of EEG-based biometric systems and problems related to designing, developing, and deploying new security-related BCI applications, for example, for personal authentication on mobile devices, augmented and virtual reality, headsets, and Internet. This special issue is aimed to introduce the recent advances of EEG-based biometrics and addresses the challenges in developing the EEG-based biometry systems for various practical applications, while proposing new ideas and directions for future development, such as data preprocessing, feature extraction, recognition, and matching; signal processing and machine learning techniques; EEG biometric based passwords and encryption; cancellable EEG biometrics; multimodal (EEG, EMG, ECG, and other biosignals) biometrics; pattern recognition techniques; protocols, standards, and interfaces; security and privacy; information fusion for biometrics involving EEG data, virtual environment applications, stimuli sets, and passive BCI technology.