Voice and Brainwaves: An Integrated Approach to Human Identification

Abstract

Electroencephalography (EEG) and voice biometrics are emerging technologies in human identification and authentication. EEG measures the electrical activity produced by neural oscillations in the brain and offers a unique and challenging-to-replicate biometric identifier. Voice biometrics, on the other hand, analyzes an individual's vocal characteristics, providing a convenient and effective method for identification. Combining voice and EEG biometrics enhances security and robustness by leveraging the unique features of both the modalities. EEG captures distinct brainwave patterns, whereas voice biometrics utilize the unique qualities of speech, such as pitch, tone, and rhythm. This study presents a method for combining voice and EEG features to develop a highly secure biometric system. Using the Linear Discriminant Analysis (LDA) technique, the system integrates the distinctive characteristics of an individual's voice and brainwaves. The combination of these biometrics not only improves the accuracy of identification but also significantly enhances security, making it extremely difficult for unauthorized individuals to replicate both voice and EEG patterns simultaneously. The proposed system achieved an impressive accuracy rate of 98.7%, demonstrating the effectiveness of combining voice and EEG biometrics. This high level of accuracy and security makes it suitable for applications in high-security environments such as military installations and secure data centers, as well as for personal device security. By integrating these advanced biometric techniques, the proposed system offers a robust and reliable method of human identification and authentication.