Biometric Identification Based on EEG Using Fuzzy Logic: A Novel Approach with Metaheuristic Optimization

Biometric identification involves using person recognition techniques to extract physical or biological traits. These traits make it possible to characterize and differentiate one person from another and provide crucial and irreplaceable information. Biometric identification based on EEG signals has recently become very interesting because it cannot be falsified and provides proof of life in security applications. An EEG-based biometric system is a complex one from which the defining characteristics of each person are extracted. Still, it is not free of uncertainties and impressions inherent to the measurement technique itself. This paper explores the potential of using a classification based on fuzzy logic in conjunction with a particle swarm optimization algorithm (PSO). The methodology was tested with two databases of 13 and 109 subjects, with results showing that this methodology allows high accuracy ratios of 99%. The study provides evidence of the potential of this approach for practical applications in biometric identification.