Task sensitivity in EEG biometric recognition

This work explores the sensitivity of electroencephalographic-based biometric recognition to the type of tasks required by subjects to perform while their brain activity is being recorded. A novel wavelet-based feature is used to extract identity information from a database of 109 subjects who performed four different motor movement/imagery tasks while their data were recorded. Training and test of the system was performed using a number of experimental protocols to establish if training with one type of task and tested with another would significantly affect the recognition performance. Also, experiments were conducted to evaluate the performance when a mixture of data from different tasks was used for training. The results suggest that performance is not significantly affected when there is a mismatch between training and test tasks. Furthermore, as the amount of data used for training is increased using a combination of data from several tasks, the performance can be improved. These results indicate that a more flexible approach may be incorporated in data collection for EEG-based biometric systems which could facilitate their deployment and improved performance.

[1]  S. Luck.  An Introduction to the Event-Related Potential Technique , 2005 .

[2]  Heung-Il Suk,et al.  Eeg-based person authentication using face stimuli , 2013, 2013 International Winter Workshop on Brain-Computer Interface (BCI).

[3]  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).

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

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

[6]  Vassilios Chrissikopoulos,et al.  Parametric person identification from the EEG using computational geometry , 1999, ICECS'99. Proceedings of ICECS '99. 6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.99EX357).

[7]  R. Palaniappan,et al.  Identifying Individuality Using Mental Task Based Brain Computer Interface , 2005, 2005 3rd International Conference on Intelligent Sensing and Information Processing.

[8]  S. Mallat.  VI – Wavelet zoom , 1999 .

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

[10]  Su Yang,et al.  Novel HHT-Based Features for Biometric Identification Using EEG Signals , 2014, 2014 22nd International Conference on Pattern Recognition.

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

[12]  Sharath Pankanti,et al.  Biometrics: a grand challenge , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[13]  D. Lee Fugal,et al.  Conceptual wavelets in digital signal processing : an in-depth, practical approach for the non-mathematician , 2009 .

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

[15]  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).

[16]  N. Kawabata.  A nonstationary analysis of the electroencephalogram. , 1973, IEEE transactions on bio-medical engineering.

[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]  Danilo P. Mandic,et al.  EEG Based Biometric Framework for Automatic Identity Verification , 2007, J. VLSI Signal Process..

[19]  Mika P. Tarvainen,et al.  Estimation of nonstationary EEG with Kalman smoother approach: an application to event-related synchronization (ERS) , 2004, IEEE Transactions on Biomedical Engineering.

[20]  Dat Tran,et al.  Human identification with electroencephalogram (EEG) signal processing , 2012, 2012 International Symposium on Communications and Information Technologies (ISCIT).

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

[22]  Ramaswamy Palaniappan,et al.  A new method to identify individuals using signals from the brain , 2003, Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint.

[23]  M. Kostilek,et al.  EEG biometric identification: Repeatability and influence of movement-related EEG , 2012, 2012 International Conference on Applied Electronics.

[24]  RP Dum,et al.  Topographic organization of corticospinal projections from the frontal lobe: motor areas on the medial surface of the hemisphere , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[25]  J. Polich.  Updating P300: An integrative theory of P3a and P3b , 2007, Clinical Neurophysiology.

[26]  R. Homan,et al.  Cerebral location of international 10-20 system electrode placement. , 1987, Electroencephalography and clinical neurophysiology.

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

[28]  Oded Ghitza,et al.  Hidden Markov models with templates as non-stationary states: an application to speech recognition , 1993, Comput. Speech Lang..

[29]  E Donchin,et al.  The mental prosthesis: assessing the speed of a P300-based brain-computer interface. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

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

[31]  Gerard J. Tortora,et al.  Principles of anatomy and physiology: volume 2, maintenance and continuity of the human body. , 2011 .

[32]  Su Yang,et al.  Wavelet-Based EEG Preprocessing for Biometric Applications , 2013, 2013 Fourth International Conference on Emerging Security Technologies.

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

[34]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[35]  J. G. Snodgrass,et al.  A standardized set of 260 pictures: norms for name agreement, image agreement, familiarity, and visual complexity. , 1980, Journal of experimental psychology. Human learning and memory.

[36]  RP Dum,et al.  Topographic organization of corticospinal projections from the frontal lobe: motor areas on the lateral surface of the hemisphere , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

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

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

[39]  Ramaswamy Palaniappan,et al.  Individual identification technique using visual evoked potential signals , 2002 .

[40]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[41]  G.K. Singhal,et al.  Person Identification Using Evoked Potentials and Peak Matching , 2007, 2007 Biometrics Symposium.

[42]  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).

[43]  Vassilios Chrissikopoulos,et al.  Person identification based on parametric processing of the EEG , 1999, ICECS'99. Proceedings of ICECS '99. 6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.99EX357).