Brain-controlled operator model-driven deep reinforcement learning for adaptive brain-machine collaborative control

doi:10.1016/j.eswa.2025.130770

2025-12-22

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DOI：10.1016/j.eswa.2025.130770

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摘要

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• 提出了一种基于深度强化学习的自适应脑机协同控制方法。 • 开发了一种脑控操作员模型，以将人类决策经验嵌入学习过程。 • 设计了双Actor-Critic机制用于自适应人机协调。 • 在不确定性和低EEG精度条件下增强了适应性和鲁棒性。 • 实验表明其性能、稳定性和自主性优于基线方法。

期刊

Expert Systems with Applications

IF:

7.5

论文数:

3.0W

被引数:

10.2W

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暂无机构信息

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