Integrating Brainwave Analysis and Facial Detection for Enhanced Security in Transactional Systems

This paper proposes an innovative user authentication system tailored for high-value asset transactions, leveraging advancements in brainwave analysis and emotional state detection. Traditional authentication methods are increasingly vulnerable to sophisticated attacks, necessitating novel approaches to bolster security in transactional systems. Our research explores the untapped potential of Electroencephalography (EEG) data to establish a unique user identifier based on distinctive brainwave patterns, including alpha, beta, gamma, delta, and theta waves. We identify specific patterns associated with the user's relaxed state through rigorous analysis, presenting a robust and bypass-resistant authentication factor. Furthermore, our system integrates facial expression recognition technology to detect negative emotional states, such as stress or panic, during transactional interactions. By combining brainwave analysis with facial recognition, our multi-layered approach enhances security by considering both physiological and emotional indicators. If the brainwave analysis identifies a relaxed state while facial recognition detects negative emotions, the system identifies potential coercion attempts and restricts transaction completion, thus safeguarding against fraudulent activities.