Person Identification by Using AR Model for EEG Signals
Behnam Molaee Ardekani;
Mohammad B. Shamsollahi
A direct connection between ElectroEncephaloGram
(EEG) and the genetic information of individuals has been
investigated by neurophysiologists and psychiatrists since 1960-s;
and it opens a new research area in the science. This paper focuses on
the person identification based on feature extracted from the EEG
which can show a direct connection between EEG and the genetic
information of subjects. In this work the full EO EEG signal of
healthy individuals are estimated by an autoregressive (AR) model
and the AR parameters are extracted as features. Here for feature
vector constitution, two methods have been proposed; in the first
method the extracted parameters of each channel are used as a
feature vector in the classification step which employs a competitive
neural network and in the second method a combination of different
channel parameters are used as a feature vector. Correct classification
scores at the range of 80% to 100% reveal the potential of our
approach for person classification/identification and are in agreement
to the previous researches showing evidence that the EEG signal
carries genetic information. The novelty of this work is in the
combination of AR parameters and the network type (competitive
network) that we have used. A comparison between the first and the
second approach imply preference of the second one.
A. Anoklin, O. Fisher, Y. Mao, P. Vogt, E. Schalt, F. Vogel,(1999), "A
genetic study of the human low-voltage electroencephalogram," Human
Genetic ,vol. 90,PP. 99-112,1992.
 M. Poulos, M. Rangoussi, V. Chrissicopoulos, A. Evagelou, " person
identification based on parametric processing on the EEG," Processings
IEE of the Sixth International Conference on Electronics, Circuits and
Systems, ICECS-99, Pafos, Cyprous. September 1999, PP. 283-286.
 M. Poulos, M. Rangoussi, V. Chrissicopoulos, A. Evagelou, "parametric
person identification from the EEG using computational geometry,"
Processings IEE of the Sixth International Conference on Electronics,
Circuits and Systems, ICECS-99, Pafos, Cyprous. September 1999, PP.
 N. Hazarika, A. Tsoi, A. Sergejew, "Nonlinear Consideration in EEG
signal classification," IEEE Transaction on Signal Processing,vol. 45,
No. 4,PP. 829-836,1997.
 N. Nielsen, B. Harvand, "The electroencephalogram in univular twins
brought up apart", Acta Genetica,vol. 8,PP. 57-64,1958.
 T. Kohonen, "Self-organization and associative memory," 3rd ed.,
Springer-Verlag, New York, 1988
 R. Plomin , "The role of inheritance in behavior," Science, vol. 248, PP.
 W. Lennox, E. Gibbs, F. Gibbs, "The brain-patern, an hereditary trail,"
The Journal of Heredity, vol. 36, PP. 233-243, 1945.
 M. Poulos, M. Rangoussi, E. Kafetzopoulos, "Person identification via
the EEG using computational geometry algorithms," Proc. Intl. Conf.
EUSIPCO-98, Rhodes, Greece, Sept. 1998.
 H. H. Stassen, G. Bomben, P. Propping, "Genetic aspects of the EEG: an
investigation into the withinpair similarity of monozygotic and dizygotic
twins with a new method of analysis," Electroencephalography and
Clinical Neurophysiology,vol. 66, PP. 489-581, 1987.
 N.E. Sviderskaya, T.A. Korolkova, "Genetic Features of spatial
organization the human cerebral cortex," Neuroscience and Behavioral
Physiology, vol. 25, no. 5, 1995.
 J. Varner, R. Potter, J. Rohrbaugh, "A procedure for automatic
classification of EEG genetic variants," Processing of Biological
Signals, 30.9-3, Annual International Conference of the IEEE
Engineering in Medicine and Biology Society,vol. 13, no. 1,1991.
 F. Vogel, "The genetic basis of the normal EEG," Human Genetic, vol.
10, PP. 91-114, 1970.
A. Kasmia (Neurologist), "Personal Comunication," Oct.2000. Internet
A.K.Jain (supervisor), "Biometrics Homepage Michigan State
A.Remond(editor) "EEG informatics. A didactic review of methods and
applications of EEG data processing," Elsevier Sientific Publishing
M.Poulos, M.Rangoussi, N.Alexandries, "Neural network based person
identification using EEG features," Proceeding IEEE of the international
conference on Acoustic , Speech,and Signal Processing,
ICASSP-99,Arizona,USA,March 1999,PP. 1117-1120
R.Paranjape,J.Mahovsky,L.Benedicenti , "On AR model and other
metrics of the EEG for Subject identification," Internet.
R.Schmidt , G.Thews " Integrative function of Nervous System,"
S.Jain,G.Deshpande, "Parametric Modeling of Brain Signals," Internet.
S.Y.Kung, "digital Neural Netwok," (PTR Prentic Hall,Englewood
Cliffs,New Jersey 1993)