Multi-Armed Bandits in Brain-Computer Interfaces
(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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- author
- Heskebeck, Frida LU ; Bergeling, Carolina and Bernhardsson, Bo LU
- organization
- publishing date
- 2022-07-05
- 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}}, }