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Personalized adaptive instruction design (PAID) for brain–computer interface using reinforcement learning and deep learning: simulated data study

Personalized adaptive instruction design (PAID) for brain–computer interface using reinforcement... Brain–computer interface (BCI) systems may require the user to perform a set of mental tasks, such as imagining different types of motion. The performance demonstrated on these tasks varies with time and between users. This study presents a new method for the automatically adaptive, user-specific generation of a sequence of tasks to increase the effectiveness of user training. For this purpose, we developed the Personalized Adaptive Instruction Design (PAID) algorithm, which uses reinforcement learning and deep learning. Using simulated data, we compared the training strategy developed here with uniform random and sequential selection strategies. The results demonstrate that the PAID strategy outperforms the others and is close to the theoretically optimal solution. Moreover, our algorithm offers the possibility of efficiently integrating psychological aspects of the training process into the generated strategy. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Brain-Computer Interfaces Taylor & Francis

Personalized adaptive instruction design (PAID) for brain–computer interface using reinforcement learning and deep learning: simulated data study

Brain-Computer Interfaces , Volume 6 (1-2): 13 – Apr 3, 2019

Personalized adaptive instruction design (PAID) for brain–computer interface using reinforcement learning and deep learning: simulated data study

Abstract

Brain–computer interface (BCI) systems may require the user to perform a set of mental tasks, such as imagining different types of motion. The performance demonstrated on these tasks varies with time and between users. This study presents a new method for the automatically adaptive, user-specific generation of a sequence of tasks to increase the effectiveness of user training. For this purpose, we developed the Personalized Adaptive Instruction Design (PAID) algorithm, which uses...
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Publisher
Taylor & Francis
Copyright
© 2019 Informa UK Limited, trading as Taylor & Francis Group
ISSN
2326-2621
eISSN
2326-263x
DOI
10.1080/2326263X.2019.1651570
Publisher site
See Article on Publisher Site

Abstract

Brain–computer interface (BCI) systems may require the user to perform a set of mental tasks, such as imagining different types of motion. The performance demonstrated on these tasks varies with time and between users. This study presents a new method for the automatically adaptive, user-specific generation of a sequence of tasks to increase the effectiveness of user training. For this purpose, we developed the Personalized Adaptive Instruction Design (PAID) algorithm, which uses reinforcement learning and deep learning. Using simulated data, we compared the training strategy developed here with uniform random and sequential selection strategies. The results demonstrate that the PAID strategy outperforms the others and is close to the theoretically optimal solution. Moreover, our algorithm offers the possibility of efficiently integrating psychological aspects of the training process into the generated strategy.

Journal

Brain-Computer InterfacesTaylor & Francis

Published: Apr 3, 2019

Keywords: Brain-computer interface; instructional design; learning strategy; reinforcement learning; deep learning

References