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 th...

中文翻译：

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具有人脑协作的大脑状态评估网络的自适应共享控制。

目的。对于共享控制系统，如何在机器人自主权和操作员之间权衡控制权是一个重要问题，尤其是对于基于BCI的系统而言。但是，大多数现有的共享控制器对由不同水平的大脑控制能力的受试者引起的影响的关注较少。方法。本文提出了一种称为BSE-NET的大脑状态评估网络，该网络基于量化的注意门控核强化学习在线评估受试者的大脑控制能力。利用BSE-NET的输出（置信度分数），可以设计一个共享控制器来动态调整机器人自主权和操作员之间的控制权重。主要结果。实验结果表明，大多数受试者获得了大约90％的高且稳定的实验成功率。此外，