RT info:eu-repo/semantics/article
T1 Selection of the minimum number of EEG sensors to guarantee biometric identification of individuals
A1 Gómez González, José Francisco
A1 
A1 
A2 Ingeniería Industrial
K1 EEG
K1 biometrics
K1 brain–computer interface (BCI)
K1 support vector machine (SVM)
K1 phase locking value (PLV)
K1 asymmetry index (AI)
AB Biometric identification uses person recognition techniques based on the extraction of some of their physical or biological properties, which make it possible to characterize and differentiate one person from another and provide irreplaceable and critical information that is suitable for applicationin security systems. The extraction of information from the electrical biosignal of the human brain has received a great deal of attention in recent years. Analysis of EEG signals has been widely used over the last century in medicine and as a basis for brain–machine interfaces (BMIs). In addition, theapplication of EEG signals for biometric recognition has recently been demonstrated. In this context, EEG-based biometric systems are often considered in two different applications: identification (oneto-many classification) and authentication (one-to-one or true/false classification). In this article, weestablish a methodology for selecting and reducing the minimum number of EEG sensors necessary to carry out effective biometric identification of individuals. Two methodologies were applied, one based on principal component analysis and the other on the Wilcoxon signed-rank test in order toreduce the number of electrodes. This allowed us to identify, according to the methodology used, the areas of the cerebral cortex that would allow selection of the minimum number of electrodes necessary for the identification of individuals. The methodologies were applied to two databases, onewith 13 people with self-collected recordings using low-cost EEG equipment, EMOTIV EPOC+, and another publicly available database with recordings from 109 people provided by the PhysioNet BCI.
YR 2023
FD 2023
LK http://riull.ull.es/xmlui/handle/915/39038
UL http://riull.ull.es/xmlui/handle/915/39038
LA en
DS Repositorio institucional de la Universidad de La Laguna
RD 27-dic-2024