Towards biofeedback-controlled self-rewarding learning with mobile devices

Over the past decades, reinforcement learning has been applied in several fields of computer science (especially in machine learning) as a learning algorithm. The same model can be used in cognitive neuroscience. In this case, neurotransmitters are providing the rewarding signal, and therefore in the background a similar learning process occurs as in reinforcement learning. According to previous studies, the same chemicals are produced through playing video games. In this paper, we aim to show a few methods and promising approaches to advance a biofeedback-controlled self-rewarding learning framework, which can be established in numerous applications. In recent times, mobile devices (phones and tablets as well) acquire more and more popularity among teenagers. Our objective is to develop a framework on these devices to measure and interpret neural activity and learning progress in general, so a related system can be developed to sustain attention and as a result, adaptive computer games may be developed in the near future.