Cryptographic Algorithm Designed by Extracting Brainwave Patterns

Marius-Alin Dragu, Irina-Emilia Nicolae and Mădălin-Corneliu Frunzete ()
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Marius-Alin Dragu: Department of Bioengineering and Biotechnology Engineering, Faculty of Medical Engineering, National University of Science and Technology Politehnica, 060042 Bucharest, Romania
Irina-Emilia Nicolae: Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, National University of Science and Technology Politehnica, 060042 Bucharest, Romania
Mădălin-Corneliu Frunzete: Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, National University of Science and Technology Politehnica, 060042 Bucharest, Romania

Mathematics, 2024, vol. 12, issue 13, 1-23

Abstract: A new authentication method based on EEG signal is proposed here. Biometric features such as fingerprint scanning, facial recognition, iris scanning, voice recognition, and even brainwave patterns can be used for authentication methods. Brainwave patterns, also known as brain biometrics, can be captured using technologies like electroencephalography (EEG) to authenticate a user based on their unique brain activity. This method is still in the research phase and is not yet commonly used for authentication purposes. Extracting EEG features for authentication typically involves signal processing techniques to analyze the brainwave patterns. Here, a method based on statistics for extracting EEG features is designed to extract meaningful information and patterns from the brainwave data for various applications, including authentication, brain–computer interface systems, and neurofeedback training.

Keywords: user authentication; EEG signals; visual evoked potentials (VEPs); local neighbor descriptive pattern (LNDP); personalized electrode selection algorithm; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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