Bayesian Networks for Brain-Computer Interfaces: A Survey

TL;DR

This survey reviews the application of Bayesian Networks in Brain-Computer Interfaces, highlighting their role in modeling uncertainty and complexity, and classifying related models and algorithms used in BCI research.

Contribution

It provides a comprehensive classification and summary of Bayesian Network models and algorithms applied to BCI, offering a high-level overview of the field.

Findings

Bayesian Networks effectively model uncertainty in BCI systems.

Various models and algorithms are classified and summarized.

Bayesian Networks are increasingly adopted in BCI research.

Abstract

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.