11,871 research outputs found
PIN generation using EEG : a stability study
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 brainâcomputer
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
Human brain distinctiveness based on EEG spectral coherence connectivity
The use of EEG biometrics, for the purpose of automatic people recognition,
has received increasing attention in the recent years. Most of current analysis
rely on the extraction of features characterizing the activity of single brain
regions, like power-spectrum estimates, thus neglecting possible temporal
dependencies between the generated EEG signals. However, important
physiological information can be extracted from the way different brain regions
are functionally coupled. In this study, we propose a novel approach that fuses
spectral coherencebased connectivity between different brain regions as a
possibly viable biometric feature. The proposed approach is tested on a large
dataset of subjects (N=108) during eyes-closed (EC) and eyes-open (EO) resting
state conditions. The obtained recognition performances show that using brain
connectivity leads to higher distinctiveness with respect to power-spectrum
measurements, in both the experimental conditions. Notably, a 100% recognition
accuracy is obtained in EC and EO when integrating functional connectivity
between regions in the frontal lobe, while a lower 97.41% is obtained in EC
(96.26% in EO) when fusing power spectrum information from centro-parietal
regions. Taken together, these results suggest that functional connectivity
patterns represent effective features for improving EEG-based biometric
systems.Comment: Key words: EEG, Resting state, Biometrics, Spectral coherence, Match
score fusio
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