The electroencephalogram as a biometric

Author

Paranjape, R.B. ; Mahovsky, J. ; Benedicenti, L. ; Koles, Z.

Author_Institution

Regina Univ., Sask., Canada

Volume

2

fYear

2001

fDate

2001

Firstpage

1363

Abstract

This paper examines the effectiveness electroencephalogram (EEG) as a biometric identification of individual subjects in a pool of 40 normal subjects. The EEG´s second order statistics are computed using autoregressive models of various order. The coefficients in these models are then evaluated for their biometric potential. Discriminant functions applied to the model coefficients are used to examine the degree to which the subjects in the data pool can be identified. The results indicate that the EEG has significant biometric potential. In this data pool, 100% of subjects are correctly classified when all data is used, and over 80% when the functions are computed from half the data and then applied to the remaining

Keywords

electroencephalography; identification; medical signal processing; signal classification; EEG; autoregressive models; biometric identification; biometric potential; classification; discriminant functions; electroencephalogram; model coefficients; second order statistics; Biomedical engineering; Biometrics; Brain modeling; Electric variables measurement; Electrodes; Electroencephalography; Fingerprint recognition; Humans; Magnetic field measurement; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Computer Engineering, 2001. Canadian Conference on

Conference_Location

Toronto, Ont.

ISSN

0840-7789

Print_ISBN

0-7803-6715-4

Type

conf

DOI

10.1109/CCECE.2001.933649

Filename

933649