On the Individuality of Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
A Fast Simplified Fuzzy ARTMAP Network
Neural Processing Letters
Biometric Systems: Technology, Design and Performance Evaluation
Biometric Systems: Technology, Design and Performance Evaluation
IEEE Transactions on Neural Networks
A fuzzy ARTMAP nonparametric probability estimator for nonstationary pattern recognition problems
IEEE Transactions on Neural Networks
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
A sequential procedure for individual identity verification using ECG
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
Review article: Human scalp EEG processing: Various soft computing approaches
Applied Soft Computing
Estimation of event related potentials using wavelet denoising based method
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
A comparative study of wavelet families for classification of wrist motions
Computers and Electrical Engineering
Multimodal biometric system combining ECG and sound signals
Pattern Recognition Letters
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In earlier papers, it was shown that recognizing persons using their brain patterns evoked during visual stimulus is possible. In this paper, several modifications are proposed to improve the recognition accuracy. In the method, gamma band spectral power (GBSP) features were computed from the visual evoked potential (VEP) signals recorded from 61 electrodes while subjects perceived a picture. Two methods were used to improve the classification rate. First, principal component analysis (PCA) was used to reduce the noise and background electroencephalogram (EEG) effects from the VEP signals. Second, the GBSP of each channel was normalized by the total GBSP from all the channels. Three classifiers were used: simplified fuzzy ARTMAP (SFA), linear discriminant (LD) and k-nearest neighbor (kNN). The experimental results using 800 VEP signals from 20 subjects with leave-one-out cross-validation strategy showed that PCA improves the classification performance for all the classifiers with normalization giving improved results in certain cases. The best classification performance of 96.50% obtained using the improved method shows that brain signals have suitable biometric properties that could be further exploited.