Machine Learning Meets EEG: A Novel Approach to PGA-Based Authentication Systems

The development of secure and effective identification/authentication systems is an ongoing challenge in the field of computer science, as new techniques and methods continue to emerge. In the context of this ever-evolving landscape, we propose a new multi-factor authentication system. The proposed system combines Picture Gesture Authentication (PGA) with simultaneous acquisition of users' brain signals (EEG) to enhance security. To assess the efficacy of the proposed model, we collected data from 14 individuals, each of whom participated in 20 trials to create a single-person authentication system. Features were extracted and multiple machine learning algorithms were trained and tested in three different scenarios. The first scenario utilized the created dataset to find the most effective machine learning algorithms and features; the second applied augmentation techniques for a more balanced dataset; the third used only the most important features. The results were encouraging, indicating that the proposed model works well in enhancing the level of security.