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Multi-Armed Bandits in Brain-Computer Interfaces

Heskebeck, Frida LU orcid ; Bergeling, Carolina and Bernhardsson, Bo LU orcid (2022) In Frontiers in Human Neuroscience 16.
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 describe the fruitful area of MABs to the BCI community. The review includes a background on MAB problems and standard solution methods, and... (More)

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 describe the fruitful area of 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.

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Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Brain-Computer Interface (BCI), calibration, multi-armed bandit (MAB), real-time optimization, reinforcement learning
in
Frontiers in Human Neuroscience
volume
16
article number
931085
publisher
Frontiers Media S. A.
external identifiers
  • pmid:35874164
  • scopus:85134513425
ISSN
1662-5161
DOI
10.3389/fnhum.2022.931085
project
Optimizing the Next Generation Brain Computer Interfaces using Cloud Computing
language
English
LU publication?
yes
additional info
Funding Information: This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation. All authors are also members of the ELLIIT Strategic Research Area. Publisher Copyright: Copyright © 2022 Heskebeck, Bergeling and Bernhardsson.
id
eec4fad5-4b67-4c43-b0c2-e77628410098
date added to LUP
2022-08-08 12:09:00
date last changed
2024-04-18 13:00:25
@article{eec4fad5-4b67-4c43-b0c2-e77628410098,
  abstract     = {{<p>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 describe the fruitful area of 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.</p>}},
  author       = {{Heskebeck, Frida and Bergeling, Carolina and Bernhardsson, Bo}},
  issn         = {{1662-5161}},
  keywords     = {{Brain-Computer Interface (BCI); calibration; multi-armed bandit (MAB); real-time optimization; reinforcement learning}},
  language     = {{eng}},
  month        = {{07}},
  publisher    = {{Frontiers Media S. A.}},
  series       = {{Frontiers in Human Neuroscience}},
  title        = {{Multi-Armed Bandits in Brain-Computer Interfaces}},
  url          = {{http://dx.doi.org/10.3389/fnhum.2022.931085}},
  doi          = {{10.3389/fnhum.2022.931085}},
  volume       = {{16}},
  year         = {{2022}},
}