This article discusses practical and theoretical aspects of real-time brain computer interface control methods based on Bayesian statistics. We investigate and improve the performance of automatic control and feedback algorithms of a reactive brain computer interface based on a visual oddball paradigm for faster statistical convergence. We introduce transfer learning using Gaussian mixture models, enabling a ready-to-use setup.

翻译：本文探讨了基于贝叶斯统计的实时脑机接口控制方法的实践与理论问题。我们研究并改进了基于视觉怪异范式的反应式脑机接口自动控制与反馈算法的性能，以实现更快速的统计收敛。通过引入基于高斯混合模型的迁移学习，我们实现了即用型系统配置。