This paper presents a novel biometric identification method based on beta brainwave activity, offering a unique and secure alternative to traditional biometric systems. Beta brainwaves, associated with active concentration and cognitive processes, are utilized in this research as the primary biometric feature due to their distinct patterns across individuals, making them ideal for personal identification. The proposed method integrates self-developed algorithms designed to capture, process, and analyze beta brainwave data in real-time. These algorithms have been optimized to extract unique brainwave signatures with high precision, ensuring robust identification with minimal error rates. Our application’s architecture is designed to handle the complexity of brainwave data analysis while maintaining a seamless experience for the end user. A key innovation of this method lies in the user-friendly interface of the application, which requires minimal technical expertise to operate. The system has been built with intuitive design principles, making it accessible for non-specialists. Users interact with the application via a simple setup, involving three electrodes that you put on the surface of your head. The other ends of the electrodes are connected to the EEG device we developed, which records their brainwave patterns during measurement. Preliminary tests have shown promising results, demonstrating both the accuracy of beta brainwave-based identification and the practicality of the system in real-world applications. This method introduces a new direction in biometric security, leveraging the uniqueness of brain activity to offer a secure, user-friendly, and innovative solution for personal identification.