Citation

Deng X, Yu ZL, Lin C, Gu Z, Li Y. J. Neural. Eng. 2020; ePub(ePub): ePub.

Affiliation

Automation Science and Engineering Institute, South China University of Technology, South China University of Technology, Guangzhou 510641, China, Guangzhou, CHINA.

Copyright

(Copyright © 2020, Institute of Physics Publishing)

DOI

10.1088/1741-2552/ab937e

PMID

32413885

Abstract

OBJECT: For the shared control systems, how to trade off the control weight between robot autonomy and human operator is an important issue, especially for BCI-based systems. However, most of existing shared controllers have paid less attention to the effects caused by subjects with different levels of brain control ability. APPROACH: In this paper, a brain state evaluation network, termed BSE-NET, is proposed to evaluate subjects' brain control ability online based on quantized attention-gated kernel reinforcement learning (QAGKRL). With the output of BSE-NET (confidence score), a shared controller is designed to dynamically adjust the control weight between robot autonomy and human operator. MAIN RESULTS: The experimental results show that most of subjects achieved high and stable experimental success rate of approximately 90%. Furthermore, for subjects with different accuracy on EEG decoding, a proper confidence score can be dynamically generated to reflect their levels of brain control ability, and the proposed system can effectively adjust the control weight in all-time shared control. SIGNIFICANCE: We discuss how our proposed method shows promise for BCI applications that can evaluate subjects' brain control ability online as well as provide a method for the research on self-adaptive shared control to adaptively balance control weight between subject's instruction and robot autonomy.

© 2020 IOP Publishing Ltd.

Language: en

Keywords

Brain computer interface; Brain control ability; Human-wheelchair cooperation; Self-adaptive shared control