@article{ref1,
title="Self-adaptive shared control with brain state evaluation network for human-wheelchair cooperation",
journal="Journal of neural engineering",
year="2020",
author="Deng, Xiaoyan and Yu, Zhu Liang and Lin, Canguang and Gu, Zhenghui and Li, Yuanqing",
volume="ePub",
number="ePub",
pages="ePub-ePub",
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.<br><br>© 2020 IOP Publishing Ltd.<p /> <p>Language: en</p>",
language="en",
issn="1741-2560",
doi="10.1088/1741-2552/ab937e",
url="http://dx.doi.org/10.1088/1741-2552/ab937e"
}