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Review
. 2018 Oct 9:12:66.
doi: 10.3389/fninf.2018.00066. eCollection 2018.

Challenges and Future Perspectives on Electroencephalogram-Based Biometrics in Person Recognition

Hui-Ling Chan  1 , Po-Chih Kuo  1 , Chia-Yi Cheng  2 , Yong-Sheng Chen  1   2   3
Affiliations
Review

Challenges and Future Perspectives on Electroencephalogram-Based Biometrics in Person Recognition

Hui-Ling Chan et al. Front Neuroinform. .

Abstract

The emergence of the digital world has greatly increased the number of accounts and passwords that users must remember. It has also increased the need for secure access to personal information in the cloud. Biometrics is one approach to person recognition, which can be used in identification as well as authentication. Among the various modalities that have been developed, electroencephalography (EEG)-based biometrics features unparalleled universality, distinctiveness and collectability, while minimizing the risk of circumvention. However, commercializing EEG-based person recognition poses a number of challenges. This article reviews the various systems proposed over the past few years with a focus on the shortcomings that have prevented wide-scale implementation, including issues pertaining to temporal stability, psychological and physiological changes, protocol design, equipment and performance evaluation. We also examine several directions for the further development of usable EEG-based recognition systems as well as the niche markets to which they could be applied. It is expected that rapid advancements in EEG instrumentation, on-device processing and machine learning techniques will lead to the emergence of commercialized person recognition systems in the near future.

Keywords: biometrics; electroencephalography (EEG); person authentication; person identification; person recognition.

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Figures

Figure 1
Figure 1
Architecture of person identification and person authentication systems based on electroencephalography (EEG) biometrics.
Figure 2
Figure 2
Longitudinal variations in correct recognition rate (CRR) using two-stage person identification system with/without incremental learning, depicted by orange diamonds and blue triangles, respectively (figure depicted according to the results of self-paced finger movement experiment presented in our previous work, Cheng, 2013).
Figure 3
Figure 3
Diagram of feature augmentation using prediction model (age taken as example of factor affecting EEG features).
Figure 4
Figure 4
Diagram of data processing flow in multimodal biometric system (enrollment procedure not shown).
Figure 5
Figure 5
Diagram of four machine learning techniques: incremental learning, Deep learning (DL), transfer learning and manifold learning, which can be applied in EEG-based biometrics.
Figure 6
Figure 6
Diagram of data processing flow in two-stage person identification system. Note that the enrollment procedure is not shown in this figure. The figure was depicted based on the concept of data processing flow presented in our previous work (Cheng, 2013).
See this image and copyright information in PMC

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