Multi-factor EEG-based user authentication

Electroencephalography (EEG) signal has been used widely in health and medical fields. It is also used in brain-computer interface (BCI) systems for humans to continuously control mobile robots and wheelchairs. Recently, the research communities successfully explore the potential of using EEG as a new type of biometrics in user authentication. EEG-based user authentication systems have the combined advantages of both password-based and biometric-based authentication systems, yet without their drawbacks. In this paper, we propose to take the advantage of rich information, such as age and gender, carried by EEG signals for user authentication in multi-level security systems. Our experiments showed very promising results for the proposed multi-factor EEG-based authentication method.

[1]  Alvin F. Martin,et al.  The DET curve in assessment of detection task performance , 1997, EUROSPEECH.

[2]  Jian-feng Hu,et al.  Biometric System Based on EEG Signals by Feature Combination , 2010, 2010 International Conference on Measuring Technology and Mechatronics Automation.

[3]  Wanli Ma,et al.  EEG-Based Age and Gender Recognition Using Tensor Decomposition and Speech Features , 2013, ICONIP.

[4]  O. Rosso,et al.  The Australian EEG Database , 2005, Clinical EEG and neuroscience.

[5]  Marios Poulos,et al.  Neural network based person identification using EEG features , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[6]  Bin He,et al.  BRAIN^COMPUTER INTERFACE , 2007 .

[7]  Z. Jane Wang,et al.  Hashing the mAR coefficients from EEG data for person authentication , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[8]  L. Benedicenti,et al.  The electroencephalogram as a biometric , 2001, Canadian Conference on Electrical and Computer Engineering 2001. Conference Proceedings (Cat. No.01TH8555).

[9]  F. Tenore,et al.  Low-cost electroencephalogram (EEG) based authentication , 2011, 2011 5th International IEEE/EMBS Conference on Neural Engineering.

[10]  Wanli Ma,et al.  Password Entropy and Password Quality , 2010, 2010 Fourth International Conference on Network and System Security.

[11]  Tuan Hoang,et al.  Multi-sphere support vector data description for brain-computer interface , 2012, 2012 Fourth International Conference on Communications and Electronics (ICCE).

[12]  S.K. Setarehdan,et al.  Fisher linear discriminant based person identification using visual evoked potentials , 2008, 2008 9th International Conference on Signal Processing.

[13]  M Poulos,et al.  Person Identification from the EEG using Nonlinear Signal Classification , 2002, Methods of Information in Medicine.

[14]  Brendan Z. Allison,et al.  Trends in BCI research: progress today, backlash tomorrow? , 2011, XRDS.

[15]  Trung Le,et al.  An optimal sphere and two large margins approach for novelty detection , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[16]  Parvathi Ambalakat.  Security of Biometric Authentication Systems , 2005 .

[17]  José del R. Millán,et al.  Person Authentication Using Brainwaves (EEG) and Maximum A Posteriori Model Adaptation , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Trung Le,et al.  EEG-Based Person Verification Using Multi-Sphere SVDD and UBM , 2013, PAKDD.

[19]  F. L. D. Silva,et al.  EEG signal processing , 2000, Clinical Neurophysiology.

[20]  Andreas Uhl,et al.  A survey on biometric cryptosystems and cancelable biometrics , 2011, EURASIP J. Inf. Secur..

[21]  Wanli Ma,et al.  Age and gender classification using EEG paralinguistic features , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

[22]  Ramaswamy Palaniappan,et al.  Two-Stage Biometric Authentication Method Using Thought Activity Brain Waves , 2008, Int. J. Neural Syst..

[23]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .