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Open AccessArticle

Reinforcement Learning Based Fast Self-Recalibrating Decoder for Intracortical Brain–Machine Interface

1
Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
2
Department of physiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
3
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
4
Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(19), 5528; https://doi.org/10.3390/s20195528
Received: 27 August 2020 / Revised: 15 September 2020 / Accepted: 22 September 2020 / Published: 27 September 2020
(This article belongs to the Special Issue Wearable Sensor for Activity Analysis and Context Recognition)
Background: For the nonstationarity of neural recordings in intracortical brain–machine interfaces, daily retraining in a supervised manner is always required to maintain the performance of the decoder. This problem can be improved by using a reinforcement learning (RL) based self-recalibrating decoder. However, quickly exploring new knowledge while maintaining a good performance remains a challenge in RL-based decoders. Methods: To solve this problem, we proposed an attention-gated RL-based algorithm combining transfer learning, mini-batch, and weight updating schemes to accelerate the weight updating and avoid over-fitting. The proposed algorithm was tested on intracortical neural data recorded from two monkeys to decode their reaching positions and grasping gestures. Results: The decoding results showed that our proposed algorithm achieved an approximate 20% increase in classification accuracy compared to that obtained by the non-retrained classifier and even achieved better classification accuracy than the daily retraining classifier. Moreover, compared with a conventional RL method, our algorithm improved the accuracy by approximately 10% and the online weight updating speed by approximately 70 times. Conclusions: This paper proposed a self-recalibrating decoder which achieved a good and robust decoding performance with fast weight updating and might facilitate its application in wearable device and clinical practice. View Full-Text
Keywords: intracortical brain–machine interface; reinforcement learning; adaptive decoder; transfer learning intracortical brain–machine interface; reinforcement learning; adaptive decoder; transfer learning
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MDPI and ACS Style

Zhang, P.; Chao, L.; Chen, Y.; Ma, X.; Wang, W.; He, J.; Huang, J.; Li, Q. Reinforcement Learning Based Fast Self-Recalibrating Decoder for Intracortical Brain–Machine Interface. Sensors 2020, 20, 5528. https://doi.org/10.3390/s20195528

AMA Style

Zhang P, Chao L, Chen Y, Ma X, Wang W, He J, Huang J, Li Q. Reinforcement Learning Based Fast Self-Recalibrating Decoder for Intracortical Brain–Machine Interface. Sensors. 2020; 20(19):5528. https://doi.org/10.3390/s20195528

Chicago/Turabian Style

Zhang, Peng; Chao, Lianying; Chen, Yuting; Ma, Xuan; Wang, Weihua; He, Jiping; Huang, Jian; Li, Qiang. 2020. "Reinforcement Learning Based Fast Self-Recalibrating Decoder for Intracortical Brain–Machine Interface" Sensors 20, no. 19: 5528. https://doi.org/10.3390/s20195528

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