Multi-Armed Bandits in Brain-Computer Interfaces

TL;DR

This paper reviews how multi-armed bandit algorithms can optimize decision-making in Brain-Computer Interfaces, highlighting their potential to improve BCI calibration and real-time performance.

Contribution

It introduces MAB concepts to the BCI community, reviews current methods, and suggests future research directions for integrating MAB in BCI systems.

Findings

MAB can enhance BCI calibration efficiency

MAB algorithms improve real-time BCI adaptation

The review identifies gaps and future research paths

Abstract

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.