CEREBRE: A Novel Method for Very High Accuracy Event-Related Potential Biometric Identification

The vast majority of existing work on brain biometrics has been conducted on the ongoing electroencephalogram. Here, we argue that the averaged event-related potential (ERP) may provide the potential for more accurate biometric identification, as its elicitation allows for some control over the cognitive state of the user to be obtained through the design of the challenge protocol. We describe the Cognitive Event-RElated Biometric REcognition (CEREBRE) protocol, an ERP biometric protocol designed to elicit individually unique responses from multiple functional brain systems (e.g., the primary visual, facial recognition, and gustatory/appetitive systems). Results indicate that there are multiple configurations of data collected with the CEREBRE protocol that all allow 100% identification accuracy in a pool of 50 users. We take this result as the evidence that ERP biometrics are a feasible method of user identification and worthy of further research.

[1]  Danilo P. Mandic,et al.  Biometrics from Brain Electrical Activity: A Machine Learning Approach , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  C. Jacques,et al.  The N170 : understanding the time-course of face perception in the human brain , 2011 .

[3]  Fei Su,et al.  EEG-based Personal Identification: from Proof-of-Concept to A Practical System , 2010, 2010 20th International Conference on Pattern Recognition.

[4]  Zhanpeng Jin,et al.  Brainprint: Assessing the uniqueness, collectability, and permanence of a novel method for ERP biometrics , 2015, Neurocomputing.

[5]  R J Zatorre,et al.  Human cortical gustatory areas: a review of functional neuroimaging data. , 1999, Neuroreport.

[6]  R. Heuser Surprise, surprise , 2014, Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions.

[7]  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.

[8]  Anastasia V Flevaris,et al.  Neural substrates of perceptual integration during bistable object perception. , 2013, Journal of vision.

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

[10]  Zhanpeng Jin,et al.  A direct comparison of active and passive amplification electrodes in the same amplifier system , 2014, Journal of Neuroscience Methods.

[11]  Krish D. Singh,et al.  Visual gamma oscillations: The effects of stimulus type, visual field coverage and stimulus motion on MEG and EEG recordings , 2013, NeuroImage.

[12]  Sarah Laszlo,et al.  Individual differences in involvement of the visual object recognition system during visual word recognition , 2015, Brain and Language.

[13]  Heung-Il Suk,et al.  Person authentication from neural activity of face-specific visual self-representation , 2013, Pattern Recognit..

[14]  Lawrence A. Farwell,et al.  Brain fingerprinting: a comprehensive tutorial review of detection of concealed information with event-related brain potentials , 2012, Cognitive Neurodynamics.

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

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

[17]  J. O'Doherty,et al.  Predictive Neural Coding of Reward Preference Involves Dissociable Responses in Human Ventral Midbrain and Ventral Striatum , 2006, Neuron.

[18]  Eelco F.M. Wijdicks,et al.  Determining brain death in adults , 1995, Neurology.

[19]  Kara D. Federmeier,et al.  The N400 as a snapshot of interactive processing: Evidence from regression analyses of orthographic neighbor and lexical associate effects. , 2011, Psychophysiology.

[20]  C Van Petten,et al.  Time course of word identification and semantic integration in spoken language. , 1999, Journal of experimental psychology. Learning, memory, and cognition.

[21]  S. Luck,et al.  How inappropriate high-pass filters can produce artifactual effects and incorrect conclusions in ERP studies of language and cognition. , 2015, Psychophysiology.

[22]  Marina Schmid,et al.  An Introduction To The Event Related Potential Technique , 2016 .

[23]  J. Movshon,et al.  Linearity and Normalization in Simple Cells of the Macaque Primary Visual Cortex , 1997, The Journal of Neuroscience.

[24]  Khalil El-Khatib,et al.  The state of the art in electroencephalogram and access control , 2013, 2013 Third International Conference on Communications and Information Technology (ICCIT).

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

[26]  B. V. K. Vijaya Kumar,et al.  Subject identification from electroencephalogram (EEG) signals during imagined speech , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[27]  Carles Grau,et al.  Unobtrusive Biometric System Based on Electroencephalogram Analysis , 2008, EURASIP J. Adv. Signal Process..

[28]  Nurul Nadia Ahmad,et al.  Analysis of the EEG Signal for a Practical Biometric System , 2010 .

[29]  Harald T. Schupp,et al.  The impact of hunger on food cue processing: An event-related brain potential study , 2009, NeuroImage.

[30]  Kara D. Federmeier,et al.  Better the DVL You Know , 2007, Psychological science.

[31]  Alumit Ishai,et al.  Sex, beauty and the orbitofrontal cortex. , 2007, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[32]  R. McCarley,et al.  Adenosine inhibition of mesopontine cholinergic neurons: implications for EEG arousal. , 1994, Science.

[33]  Patrizio Campisi,et al.  EEG biometrics for individual recognition in resting state with closed eyes , 2012, 2012 BIOSIG - Proceedings of the International Conference of Biometrics Special Interest Group (BIOSIG).

[34]  M. Rugg Event-related brain potentials dissociate repetition effects of high-and low-frequency words , 1990, Memory & cognition.

[35]  E. Donchin Presidential address, 1980. Surprise!...Surprise? , 1981, Psychophysiology.

[36]  B. O’Donnell,et al.  Active and passive P3 latency and psychometric performance: influence of age and individual differences. , 1992, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

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

[38]  Kara D. Federmeier,et al.  Never seem to find the time: evaluating the physiological time course of visual word recognition with regression analysis of single-item event-related potentials , 2014 .

[39]  Raveendran Paramesran,et al.  Exploiting the P300 paradigm for cognitive biometrics , 2012 .

[40]  Zhanpeng Jin,et al.  Assessment of permanence of non-volitional EEG brainwaves as a biometric , 2015, IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015).

[41]  Urte Roeber,et al.  Neural processing of orientation differences between the eyes' images. , 2012, Journal of vision.

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

[43]  P. Matthews,et al.  Independent anatomical and functional measures of the V1/V2 boundary in human visual cortex. , 2005, Journal of vision.

[44]  S. Muthukumaraswamy High-frequency brain activity and muscle artifacts in MEG/EEG: a review and recommendations , 2013, Front. Hum. Neurosci..