Authors: Ramaswamy Palaniappan; Kenneth Revett
Addresses: School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK ' Faculty of Informatics and Computer Science, British University in Egypt, Cairo, Egypt
Abstract: In a previous study, it has been shown that brain activity, i.e., electroencephalogram (EEG) signals, can be used to generate personal identification number (PIN). The method was based on brain-computer interface (BCI) technology using a P300-based BCI approach and showed that a single-channel EEG was sufficient to generate PIN without any error for three subjects. The advantage of this method is obviously its better fraud resistance compared to conventional methods of PIN generation such as entering the numbers using a keypad. Here, we investigate the stability of these EEG signals when used with a neural network classifier, i.e., to investigate the changes in the performance of the method over time. Our results, based on recording conducted over a period of three months, indicate that a single channel is no longer sufficient and a multiple electrode configuration is necessary to maintain acceptable performances. Alternatively, a recording session to retrain the neural network classifier can be conducted on shorter intervals, though practically this might not be viable.
Keywords: biometrics; BCI; brain-computer interface; EEG; electroencephalograms; personal identification number; PIN numbers; neural networks; brain activity; fraud resistance; multiple electrodes.
International Journal of Biometrics, 2014 Vol.6 No.2, pp.95 - 105
Available online: 23 May 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article