Person identification based on parametric processing of the EEG

Author

Poulos, M. ; Rangoussi, M. ; Chrissikopoulos, V. ; Evangelou, A.

Author_Institution

Dept. of Inf., Univ. of Piraeus, Greece

Volume

1

fYear

1999

fDate

1999

Firstpage

283

Abstract

Person identification based on parametric spectral analysis of the EEG signal is addressed in this work-a problem that has not yet been seen in a signal-processing framework, to the best of our knowledge. AR parameters are estimated from a signal containing only the alpha, rhythm activity of the EEG. These parameters are used as features in the classification step, which employs a learning vector quantizer network. The proposed method was applied on a set of real EEG recordings made on healthy individuals, in an attempt to experimentally investigate the connection between a person´s EEG and genetically-specific information. Correct classification scores at the range of 72% to 84% show the potential of our approach for person classification/identification and are in agreement with previous research showing evidence that the EEG carries genetic information

Keywords

autoregressive processes; biometrics (access control); electroencephalography; pattern classification; spectral analysis; vector quantisation; AR parameters; EEG; classification step; genetic information; learning vector quantizer network; parametric processing; person classification; person identification; rhythm activity; spectral analysis; Computational geometry; Data mining; Electroencephalography; Encoding; Feature extraction; Genetics; Informatics; Information security; Physiology; Rhythm;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Circuits and Systems, 1999. Proceedings of ICECS '99. The 6th IEEE International Conference on

Conference_Location

Pafos

Print_ISBN

0-7803-5682-9

Type

conf

DOI

10.1109/ICECS.1999.812278

Filename

812278