Computational Intelligence and Neuroscience

EEG-Based Biometrics: Challenges and Applications


1Universidade de Fortaleza, Fortaleza, Brazil

2Kaunas University of Technology, Kaunas, Lithuania

3University of Porto, Porto, Portugal

4University of Fortaleza, Fortaleza, Brazil

EEG-Based Biometrics: Challenges and Applications


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


  • Special Issue
  • - Volume 2018
  • - Article ID 5483921
  • - Editorial

EEG-Based Biometrics: Challenges And Applications

Victor Hugo C. de Albuquerque | Robertas Damaševičius | ... | Plácido R. Pinheiro
  • Special Issue
  • - Volume 2018
  • - Article ID 8041609
  • - Research Article

An Improved Multispectral Palmprint Recognition System Using Autoencoder with Regularized Extreme Learning Machine

Abdu Gumaei | Rachid Sammouda | ... | Ahmed Alsanad
  • Special Issue
  • - Volume 2018
  • - Article ID 1867548
  • - Research Article

Combining Cryptography with EEG Biometrics

Robertas Damaševičius | Rytis Maskeliūnas | ... | Marcin Woźniak
  • Special Issue
  • - Volume 2018
  • - Article ID 9672871
  • - Research Article

Single-Trial Evoked Potential Estimating Based on Sparse Coding under Impulsive Noise Environment

Nannan Yu | Ying Chen | ... | Hanbing Lu
  • Special Issue
  • - Volume 2018
  • - Article ID 5623165
  • - Research Article

The EEG Activity during Binocular Depth Perception of 2D Images

Marsel Fazlyyyakhmatov | Nataly Zwezdochkina | Vladimir Antipov
  • Special Issue
  • - Volume 2018
  • - Article ID 4613740
  • - Research Article

-Iterative Exponential Forgetting Factor for EEG Signals Parameter Estimation

Karen Alicia Aguilar Cruz | María Teresa Zagaceta Álvarez | ... | José de Jesús Medel Juárez
  • Special Issue
  • - Volume 2017
  • - Article ID 2721846
  • - Research Article

Reducing the Schizophrenia Stigma: A New Approach Based on Augmented Reality

Rafael D. de C. Silva | Saulo G. C. Albuquerque | ... | Victor Hugo C. Albuquerque

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