Identifying Individuality Using Mental Task Based Brain Computer Interface

In recent years, numerous Brain Computer Interface (BCI) technologies have been developed to assist the disabled. In this paper, mental task based BCI is proposed for a different purpose: to identify the individuality of a person. The idea is based on the classification of electroencephalogram (EEG) signals recorded when a user thinks of either one or two mental tasks. As different individuals have different thought processes, this idea would be appropriate for individual identification. To increase the inter-subject differences, EEG data from six electrodes are used instead of one. Sixth order autoregressive features are computed from EEG signals and classified by Linear Discriminant classifier using a modified 10 fold cross validation procedure, which gave an average error of 0.95% when tested on 400 EEG patterns from four subjects. Though the method would have to undergo further development to obtain repeatable good accuracy; this initial study has shown the huge potential of the method over existing biometric identification systems as it is impossible to be faked.

[1]  Sharath Pankanti,et al.  Biometrics: Future of Identification , 2000 .

[2]  Anil K. Jain,et al.  Matching of palmprints , 2002, Pattern Recognit. Lett..

[3]  S. Nishida,et al.  A new brain-computer interface design using fuzzy ARTMAP , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[4]  Sharath Pankanti,et al.  A Prototype Hand Geometry-based Verication System , 1999 .

[5]  E. A. Stolz,et al.  Multivariate Autoregressive Models for Classification of Spontaneous Electroencephalogram During Mental Tasks1 , 1998 .

[6]  S. Sengupta.  Introduction to Applied Statistical Signal Analysis , 1991 .

[7]  Ola Pettersson,et al.  ECG analysis: a new approach in human identification , 2001, IEEE Trans. Instrum. Meas..

[8]  John Daugman.  Recognising Persons by Their Iris Patterns , 2004, SINOBIOMETRICS.

[9]  M. Osaka,et al.  Peak alpha frequency of EEG during a mental task: task difficulty and hemispheric differences. , 1984, Psychophysiology.

[10]  Z. Keirn,et al.  A new mode of communication between man and his surroundings , 1990, IEEE Transactions on Biomedical Engineering.

[11]  Sharath Pankanti,et al.  On the individuality fingerprints , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[12]  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).

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

[14]  Ramaswamy Palaniappan,et al.  Method of identifying individuals using VEP signals and neural network , 2004 .

[15]  Ashok Samal,et al.  Automatic recognition and analysis of human faces and facial expressions: a survey , 1992, Pattern Recognit..

[16]  C.W. Anderson,et al.  Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks , 1998, IEEE Transactions on Biomedical Engineering.

[17]  H. Jasper,et al.  The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. , 1999, Electroencephalography and clinical neurophysiology. Supplement.