# Multi-Armed Bandits in Brain-Computer Interfaces

arXiv: https://arxiv.org/abs/2205.09584 · HTML: https://mlsanity.com/paper/2205.09584/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 42 references that are in the map.
- First posted on arXiv 19 May 2022.

## Builds on (most cited first, 4 of 4)

- [A Survey on Practical Applications of Multi-Armed and Contextual Bandits](https://mlsanity.com/paper/1904.10040/) (2019)
- [A Survey of Online Experiment Design with the Stochastic Multi-Armed Bandit](https://mlsanity.com/paper/1510.00757/) (2015)
- [Minimizing subject-dependent calibration for BCI with Riemannian transfer learning](https://mlsanity.com/paper/2111.12071/) (2021)
- [A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data](https://mlsanity.com/paper/1705.08111/) (2017)

## Built on by (most cited first, 1 of 1)

- [Sequential Best-Arm Identification with Application to Brain-Computer Interface](https://mlsanity.com/paper/2305.11908/) (2023)

## Abstract (arXiv)

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.