2017 : EEG-Based Biometrics: Challenges and Applications
|2017 : EEG-Based Biometrics: Challenges and Applications|
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|Table of Contents|
Special Issue on EEG-Based Biometrics: Challenges and Applications (Computational Intelligence and Neuroscience, IF=1.215 (Clarivate Analytics Web of Science))
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 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, VR (Virtual Reality) headsets, and Internet.
This special issue aims to introduce the recent progress of EEG-based biometrics and addresses the challenges in developing EEG-based biometry systems for various practical applications, while proposing new ideas and directions for future development.
Potential topics include but are not limited to the following:
EEG biometry Data preprocessing, feature extraction, recognition, and matching for EEG-based biometric systems Signal processing and machine learning techniques for EEG-based biometrics EEG biometric based passwords and encryption Cancellable EEG biometrics Multimodal (EEG, EMG, ECG, and other biosignals) biometrics Pattern recognition for biometrics Performance and accuracy evaluation of EEG-based biometric systems Protocols, standards, and interfaces for EEG biometrics Security and privacy of biometric EEG data Information fusion for biometrics involving EEG data EEG biometrics for VR applications Stimuli sets for EEG-based biometrics Passive BCI technology
Authors can submit their manuscripts through the Manuscript Tracking System at https://mts.hindawi.com/submit/journals/cin/eebb/.
Submission Deadline Friday, 27 October 2017 Publication Date March 2018 Papers are published upon acceptance, regardless of the Special Issue publication date.
Lead Guest Editor
Victor Hugo C. De Albuquerque, Universidade de Fortaleza, Fortaleza, Brazil
Robertas Damaševičius, Kaunas University of Technology, Kaunas, Lithuania João M. R. S. Tavares, University of Porto, Porto, Portugal Plácido R. Pinheiro, University of Fortaleza, Fortaleza, Brazil
|Acronym||EEG 2017 +|
|End date||01:34:15, 28 July 2017 +|
|Has coordinates||9° 5' 47", 76° 36' 19"Latitude: 9.0964305555556|
Longitude: 76.605413888889 +
|Has location city||N/A +|
|Has location country||Category:Undefined +|
|Submission deadline||12:00:00, 27 October 2017 +|