论文标题
大脑计算机界面中的多臂匪徒
Multi-Armed Bandits in Brain-Computer Interfaces
论文作者
论文摘要
多臂强盗（MAB）问题模型模型的决策者，基于当前并获得了新知识来优化其行动，以最大程度地提高其奖励。这种类型的在线决定在许多脑部计算机界面（BCIS）的过程中很突出，并且MAB以前已用于调查，例如，用于优化BCI性能的哪些心理命令。但是，在BCI背景下的MAB优化仍然相对尚未探索，尽管它有可能在校准和实时实现过程中提高BCI性能。因此，这篇评论旨在将mABS进一步引入BCI社区。该评论包括有关MAB问题和标准解决方案方法的背景，以及与BCI系统有关的解释。此外，它包括BCI中MAB的最先进概念以及未来研究的建议。
The multi-armed bandit (MAB) problem models a decision-maker that optimizes its actions based on current and acquired new knowledge to maximize its reward. This type of online decision is prominent in many procedures of Brain-Computer Interfaces (BCIs) and MAB has previously been used to investigate, e.g., what mental commands to use to optimize BCI performance. However, MAB optimization in the context of BCI is still relatively unexplored, even though it has the potential to improve BCI performance during both calibration and real-time implementation. Therefore, this review aims to further introduce MABs to the BCI community. The review includes a background on MAB problems and standard solution methods, and interpretations related to BCI systems. Moreover, it includes state-of-the-art concepts of MAB in BCI and suggestions for future research.
