Biometric Identification using Electroencephalography

In this paper, investigate the use of brain activity for person identification. A biometric system is a technological system that uses information about a person. Research on brain signals show that each individual has a unique brain wave pattern. Electroencephalography signals generated by mental t...

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Bibliographic Details
Published in:International journal of computer applications 2014-01, Vol.106 (15)
Main Authors: R, Hema C, A, Elakkiya, Mp, Paulraj
Format: Article
Language:English
Subjects:
Biometrics
Brain
Classification
Density
Electroencephalography
Neural networks
Signatures
Tasks
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Description
Summary:In this paper, investigate the use of brain activity for person identification. A biometric system is a technological system that uses information about a person. Research on brain signals show that each individual has a unique brain wave pattern. Electroencephalography signals generated by mental tasks are acquired to extract the distinctive brain signature of an individual. Electroencephalography signals during four biometric tasks, namely relax, math, read and spell was acquired from 50 subjects. Features are derived from power spectral density. Classification is performed using Feed forward neural network and Recurrent neural network. The performance of the neural model was evaluated in terms of training, performance and classification accuracies. The results confirmed that the proposed scheme has potential in classifying the EEG signals. RNN is considerably better with an average accuracy of 95% for the spell task and 92% for the read tasks in comparison with a feed forward neural network. The results validate the feasibility of using brain signatures for biometrics study.
ISSN:0975-8887
0975-8887
DOI:10.5120/18596-9845