论文标题
用于脑部计算机界面的贝叶斯网络：调查
Bayesian Networks for Brain-Computer Interfaces: A Survey
论文作者
论文摘要
大脑计算机界面（BCI）是一项快速开发的技术，可以在人脑和外部设备（例如机器人臂和计算机）之间进行直接通信。贝叶斯网络是机器学习的强大工具，用于解决问题，需要理解和建模由亚型组件构建的复杂系统中的不确定性和复杂性。因此，在脑部计算机界面的应用中部署贝叶斯网络成为BCI研究中越来越流行的方法。这项调查涵盖了相对高级的观点的现有作品，对所涉及的模型和算法进行了分类，还总结了贝叶斯网络或其变体在脑部计算机接口中的应用。
Brain-Computer Interface (BCI) is a rapidly developing technology that allows direct communications between the human brain and external devices, such as robotic arms and computers. Bayesian Networks is a powerful tool in machine learning for tackling with problems that requires understanding and modelling the uncertainty and complexity within complex system built by sub-modular components. Therefore, deploying Bayesian Networks in the application of Brain-Computer Interfaces becomes an increasingly popular approach in BCI research. This survey covers related existing works in relatively high-level perspectives, classifies the models and algorithms involved, and also summarizes the application of Bayesian Networks or its variants in the context of Brain-Computer Interfaces.
