New biometric approach based on motor imagery EEG signals

Author

HU, Jian-feng

Author_Institution

Inst. of Inf. Technol., Jiangxi Bluesky Univ., Nanchang, China

fYear

2009

fDate

13-14 Dec. 2009

Firstpage

94

Lastpage

97

Abstract

A research on biometry based on motor imagery EEG signals was described. In this study, I select EEG signals related to motor imagery, and a model was built. Estimated model parameters as feature vector were extracted, and then to classified by an artificial neural network. Two different classify cases, including authentication and identification, were investigated. Four types of motor imagery EEG signals and three subjects were compared. Experiment results show that EEG carrying individual-specific information can be successfully exploited for purpose of person authentication and identification.

Keywords

autoregressive moving average processes; biometrics (access control); electroencephalography; feature extraction; gait analysis; medical signal processing; neural nets; signal classification; ARMA model; artificial neural network; biometrics; feature vector extraction; motor imagery EEG signals; person authentication; person identification; signal classification; Authentication; Biomedical engineering; Biometrics; Brain modeling; Electroencephalography; Fingerprint recognition; Foot; Parameter estimation; Testing; Tongue; ARMA model; Biometric; Electroencephalogram (EEG); Nonlinear analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

BioMedical Information Engineering, 2009. FBIE 2009. International Conference on Future

Conference_Location

Sanya

Print_ISBN

978-1-4244-4690-2

Electronic_ISBN

978-1-4244-4692-6

Type

conf

DOI

10.1109/FBIE.2009.5405787

Filename

5405787