An investigation of using SSVEP for EEG-based user authentication system

User authentication system to identify individual by using electroencephalograph (EEG) feature based on steady-state visual evoked potential (SSVEP) has been proposed. Recently, SSVEP has been used as a stimulator due to it plays an important role in the response to various visual stimuli, i.e., flickering rate (F), intensity (I), and duty cycle (D). Moreover, the SSVEPs are practical and useful in research because of its excellent signal-to-noise ratio and relative immunity to artifacts and succeeded in many disciplines. In this paper, therefore, we investigate individual SSVEP corresponding to the different visual stimulation in terms of frequency component analysis in the four principal frequency bands, i.e., delta (0.1-3.5 Hz), theta (4.0-7.5 Hz), alpha (8.0-13.0 Hz), and beta (14.0-30.0 Hz). Subjects were instructed to fixate an LED light source then record associated SSVEP waveform. The variation in displaying of the presentation stimuli during a task was examined thereby demonstrating the high usability, adaptability and flexibility of the visual stimulator and determine the optimal parameters for the subject comfort. The experiment achieved the true acceptance rate of 60% to 100% approximately revealing the potential of proposed system for user classification/identification.

[1]  Wenyao Xu,et al.  Exploring EEG-based biometrics for user identification and authentication , 2014, 2014 IEEE Signal Processing in Medicine and Biology Symposium (SPMB).

[2]  Chang-Hwan Im,et al.  Development of an SSVEP-based BCI spelling system adopting a QWERTY-style LED keyboard , 2012, Journal of Neuroscience Methods.

[3]  Ramaswamy Palaniappan,et al.  Biometric Paradigm Using Visual Evoked Potential , 2009 .

[4]  Y. Nakajima,et al.  Visual stimuli for the P300 brain–computer interface: A comparison of white/gray and green/blue flicker matrices , 2009, Clinical Neurophysiology.

[5]  Mahmoud Taleb Beidokhti,et al.  Advances in Intelligent Systems and Computing , 2016 .

[6]  C. Herrmann.  Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena , 2001, Experimental Brain Research.

[7]  Julie Thorpe,et al.  Pass-thoughts: authenticating with our minds , 2005, NSPW '05.

[8]  Tien Pham,et al.  EEG-Based User Authentication Using Artifacts , 2014, SOCO-CISIS-ICEUTE.

[9]  C. Sudalaimani,et al.  Automated seizure detection from multichannel EEG signals using Support Vector Machine and Artificial Neural Networks , 2013, 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s).

[10]  Chaiwat Nuthong,et al.  Frequency component analysis of eeg recording on various visual tasks: Steady-state visual evoked potential experiment , 2015, 2015 7th International Conference on Knowledge and Smart Technology (KST).

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

[12]  Tien Pham,et al.  EEG-Based User Authentication in Multilevel Security Systems , 2013, ADMA.

[13]  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.

[14]  Charles Wang,et al.  I Think, Therefore I Am: Usability and Security of Authentication Using Brainwaves , 2013, Financial Cryptography Workshops.

[15]  W. Khalifa,et al.  A survey of EEG based user authentication schemes , 2012, 2012 8th International Conference on Informatics and Systems (INFOS).

[16]  Surej Mouli,et al.  A Configurable, Inexpensive, Portable, Multi-channel, Multi-frequency, Multi-chromatic RGB LED System for SSVEP Stimulation , 2015, Brain-Computer Interfaces.