Biometric identification of persons using sample entropy features of EEG during rest state

Biometric recognition of persons using brain waves has been identified as an attractive topic of research today. Existing popular biometric modalities of face, finger prints and voice signals are vulnerable to various kinds of attacks and spoofing techniques, whereas the emerging biometric trait extracted from brain wave is expected to act as an ideal biometric feature offering high degree of uniqueness, stability and universality. This paper analyses the efficacy of the complexity of Electroencephalogram (EEG) signals recorded during rest state for recognizing individuals from a publicly available EEG dataset consisting of 109 subjects. Sample entropy features extracted from delta, theta, alpha, beta and gamma bands of 64 channel EEG have been evaluated for subject-identification in the proposed system. It is found that beta band entropy has the highest inter-subject variability. Based on a Mahalanobis distance based classifier, beta entropy gives an average correct recognition rate of 98.31%. It has also been observed that concatenation of entropy features with power spectral density (PSD) values improves the system performance. Further analysis is essential to investigate the stability of results over time and to optimize the recognition performance at a reduced number of channels.

[1]  André Zúquete,et al.  Biometric Authentication with Electroencephalograms: Evaluation of Its Suitability Using Visual Evoked Potentials , 2010, BIOSTEC.

[2]  Sharath Pankanti,et al.  Biometric Recognition: Security and Privacy Concerns , 2003, IEEE Secur. Priv..

[3]  Mohammed Abo-Zahhad,et al.  State-of-the-art methods and future perspectives for personal recognition based on electroencephalogram signals , 2015, IET Biom..

[4]  J Wang,et al.  Feature exaction and classification of attention related electroencephalographic signals based on sample entropy , 2007 .

[5]  Gian Luca Marcialis,et al.  Minimum spanning tree and k-core decomposition as measure of subject-specific EEG traits , 2016 .

[6]  K.-R. Muller,et al.  Optimizing Spatial filters for Robust EEG Single-Trial Analysis , 2008, IEEE Signal Processing Magazine.

[7]  Xin Zhao,et al.  Visual attention recognition based on nonlinear dynamical parameters of EEG. , 2014, Bio-medical materials and engineering.

[8]  Fabio Babiloni,et al.  Human Brain Distinctiveness Based on EEG Spectral Coherence Connectivity , 2014, IEEE Transactions on Biomedical Engineering.

[9]  Kwang Suk Park,et al.  A study on the reproducibility of biometric authentication based on electroencephalogram (EEG) , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

[10]  Patrizio Campisi,et al.  EEG for Automatic Person Recognition , 2012, Computer.

[11]  Mohammed Abo-Zahhad,et al.  A Novel Biometric Approach for Human Identification and Verification Using Eye Blinking Signal , 2015, IEEE Signal Processing Letters.

[12]  Patrizio Campisi,et al.  Brain waves for automatic biometric-based user recognition , 2014, IEEE Transactions on Information Forensics and Security.

[13]  Sherif N. Abbas,et al.  A new multi-level approach to EEG based human authentication using eye blinking , 2016, Pattern Recognit. Lett..

[14]  N. Birbaumer,et al.  BCI2000: a general-purpose brain-computer interface (BCI) system , 2004, IEEE Transactions on Biomedical Engineering.

[15]  Patrizio Campisi,et al.  Stable EEG Features for Biometric Recognition in Resting State Conditions , 2013, BIOSTEC.

[16]  Brendan Z. Allison,et al.  P300 brain computer interface: current challenges and emerging trends , 2012, Front. Neuroeng..

[17]  Klaus-Robert Müller,et al.  Toward noninvasive brain-computer interfaces , 2006, IEEE Signal Process. Mag..

[18]  Madalena Costa,et al.  Multiscale entropy analysis of complex physiologic time series. , 2002, Physical review letters.

[19]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[20]  M. Faundez-Zanuy,et al.  Bioelectrical Signals as Emerging Biometrics: Issues and Challenges , 2012 .

[21]  Dong Ming,et al.  Research on Visual Attention Classification Based on EEG Entropy Parameters , 2013 .

[22]  Gian Luca Marcialis,et al.  An EEG-Based Biometric System Using Eigenvector Centrality in Resting State Brain Networks , 2015, IEEE Signal Processing Letters.