EFFICIENT CLASSIFICATION USING MULTIPLE MENTAL THOUGHTS

A. Zafar, M. I. Ahmad, M. I. Ahmad, A. Hanif, A. Hanif

Abstract


Researches in personal identification show that classification using multiple mental thoughts increases complexity and system’s processing time. In this paper, an efficient classification algorithm is proposed to classify an individual using multiple mental thoughts. Features from Electroencephalography (EEG), used as biometric, are extracted using sixth order Autoregressive (AR) model, and Linear Discriminant Analysis (LDA) based classification is performed based on best mental thought combinations. Matlab® simulation results indicate that the proposed algorithm reduces the complexity as well as the processing time that confirms the use of EEG as a biometric for personal identification.

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References


S. Pankanti, S. Prabhakar and A. K. Jain,

IEEE Trans. PAMI 24, No. 8 (2002) 1010.

J. Wayman, A. Jain, D. Maltoni and D. Maio

(Eds.), Biometric Systems: Technology,

Design and Performance Evaluation,

Springer-Verlag (2004).

A. Samal and P. Iyengar, Automatic

Recognition and Analysis of Human Faces

and Facial Expressions: A Survey, Pattern

Recognition 25, No. 1 (1992) 65.

N. Duta, A. K. Jain and K. V. Mardia, Pattern

Recognition Letters 23, No 4 (2012) 477.

J. Daugman, Recognizing Persons by Their

Iris Patterns, A.K. Jain, R. Bolle, S. Pankanti

(Eds.) : Biometrics: Personal Identification in

Networked Society. Kluwer Academic (1999).

R. Sanchez-Reillo, C. Sanchez-Avila and

Gonzalez-Macros, IEEE Trans. Pattern

Analysis and Machine Intelligence 22, No 10,

(Oct. 2000) 1168.

L. Biel, O. Pettersson, L. Philipson and

P. Wide, IEEE Trans. Instrumentation and

Measurement 50, No 3 (2001) 808.

Hurley, M. D. Nixon and J. Carter, Computer

Vision and Image Understanding 98, No. 3

(2005) 491.

R. Palaniappan and D.P. Mandic, Energy of

Brain Potential Evoked During Visual

Stimulus: A new Biometric, ICANN, LNCS

, Berlin Heidelberg (2005) pp. 735-740

R.B. Paranjape, J. Mahovsky, L. Benedicenti

and Z. Koles, The Electroencephalography

as Biometric, Proc. 2nd Canadian

Conference on Electrical and Computer

Engineering, Toronto, Ontario, Canada

(2001) pp. 1363-1366.

M. Poulos, M. Rangoussi, V. Chrissikopoulos

and A. Evangelou, Parametric Person

Identification from the EEG using

Computational Geometry 2 (1999) pp. 1005–

Nae-Jen Huan and R. Palaniappan, Journal

of Neural Engineering 1 (2004) 142.

Z. A. Keirn and J. I. Aunon, IEEE Trans.

Biomedical Engineering, 37, No. 12 (1990)

pp. 1209-1214.

R. Palaniappan, International Journal of

Information and Communication Engineering

(2006) 222.

R. Palaniappan, Electroencephalogram

Signals from Imagined Activities: A Novel

Biometric Identifier for a Small Population,

IDEAL 2006, LNCS 4224 (2006) pages 604-

R. Shiavi, “Introduction to Applied Statistical

Signal Analysis (2nd edition)”, Academic

Press, 1999.

R. Palaniappan, P. Paramesran, S. Nishida

and N. Saiwaki, IEEE Trans. Neural System

and Rehabilitation Engineering 10 (2002)

K. Fukunaga, Introduction to Statistical

Pattern Recognition, 2nd Edition. Academic

Press (1990)


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