@article{(Open Science Index):https://publications.waset.org/pdf/13020,
	  title     = {Person Identification by Using AR Model for EEG Signals},
	  author    = {Gelareh Mohammadi and  Parisa Shoushtari and  Behnam Molaee Ardekani and  Mohammad B. Shamsollahi},
	  country	= {},
	  institution	= {},
	  abstract     = {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.},
	    journal   = {International Journal of Biomedical and Biological Engineering},
	  volume    = {1},
	  number    = {11},
	  year      = {2007},
	  pages     = {621 - 625},
	  ee        = {https://publications.waset.org/pdf/13020},
	  url   	= {https://publications.waset.org/vol/11},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 11, 2007},
	}