Modelling Emotional States for Enhanced Brainwave Based Authentication Systems

Abstract

A developing biometric technique called brainwave authentication uses the distinctive electrical patterns produced by the human brain to identify a person. When it comes to security and durability, brainwave authentication surpasses standard biometrics as it utilizes an individual's own neural signatures, rather than depending on exterior physical or behavioural traits. The proposed brainwave authentication approach models the issue at hand using set theory and makes use of a tiered system design to provide quick and safe user authentication. The system uses a support vector machine (SVM) classifier to extract pertinent features from EEG signals, which allows it to differentiate between various brainwave patterns and associate them with the appropriate persons. The model's performance at identifying people based on their brainwave patterns is demonstrated by its 92% accuracy, 94% precision, 90% recall, and 92% F1 score. The suggested approach offers a viable substitute for safe and dependable user authentication across a range of domains, outperforming current brainwave authentication techniques in terms of accuracy and robustness.

Keywords: Brainwave, Brainwave Authentication, Biometrics, Cyber Security, and Emotional State