Self-adaptive shared control with brain state evaluation network for human-wheelchair cooperation

• Published in: Journal of neural engineering Vol. 17; no. 4; pp. 45005 - 45018
• Main Authors: Deng, Xiaoyan, Liang Yu, Zhu, Lin, Canguang, Gu, Zhenghui, Li, Yuanqing
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.08.2020
• Subjects: brain computer interface; brain control ability; human-wheelchair cooperation; self-adaptive shared control; Brain control ability; Brain computer interface; Self-adaptive shared control; Human-wheelchair cooperation
• ISSN: 1741-2560; 1741-2552; 1741-2552
• DOI: 10.1088/1741-2552/ab937e

Objective. 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. 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.