Automatic control of reactive brain computer interfaces

Abstract

This article discusses theoretical and practical aspects of real-time brain computer interface control methods based on Bayesian statistics. The theoretical aspects include how the data from the brain computer interface can be translated into a Gaussian mixture model that is used in the Bayesian statistics-based control methods. The practical aspects include how the control methods improve the performance of the brain computer interface. We use a reactive brain computer interface based on a visual oddball paradigm for the investigation and improvement of the performance of automatic control and feedback algorithms used in the system. By using automatic control for selection of the stimuli for the visual oddball experiment, the target stimulus is identified faster than if no automatic control is used. Finally, we introduce transfer learning using Gaussian mixture models, enabling a ready-to-use setup.