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

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.