Bellicha Angélina, Struber Lucas, Pasteau François, Juillard Violaine, Devigne Louise, Karakas Serpil, Chabardes Stephan, Babel Marie, Charvet Guillaume
University Grenoble Alpes, CEA, Leti, Clinatec, Grenoble F-38000, France.
University Rennes, INSA Rennes, Inria, CNRS, IRISA - UMR 6074, Rennes F-35000, France.
J Neural Eng. 2025 Feb 14;22(1). doi: 10.1088/1741-2552/adae36.
Assistive robots can be developed to restore or provide more autonomy for individuals with motor impairments. In particular, power wheelchairs can compensate lower-limb impairments, while robotic manipulators can compensate upper-limbs impairments. Recent studies have shown that Brain-Computer Interfaces (BCI) can be used to operate this type of devices. However, activities of daily living and long-term use in real-life contexts such as home require robustness and adaptability to complex, changing and cluttered environments which can be problematic given the neural signals that do not always allow a safe and efficient use. This article describes assist-as-needed sensor-based shared control (SC) methods relying on the blending of BCI and depth-sensor-based control.The proposed assistance targets the BCI-teleoperation of effectors for tasks that answer mobility and manipulation needs in a at-home context. The assistance provided by the proposed methods was evaluated through a wheelchair mobility and reach-and-grasp laboratory-based experiments in a controlled environment, as part of a clinical trial with a quadriplegic patient implanted with a wireless 64-channel ElectroCorticoGram recording implant named WIMAGINE.Results showed that the proposed methods can assist BCI users in both tasks. Indeed, the time to perform the tasks and the number of changes of mental tasks were reduced. Moreover, unwanted actions, such as wheelchair collisions with the environment, and gripper opening that could result in the fall of the object were avoided.The proposed methods are steps toward at-home use of BCI-teleoperated assistive robots. Indeed, the proposed SC methods improved the performance of the two assistive devices.Clinical trial, registration number: NCT02550522.
可以开发辅助机器人,以恢复或为有运动障碍的个体提供更多自主性。特别是,电动轮椅可以补偿下肢损伤,而机器人操纵器可以补偿上肢损伤。最近的研究表明,脑机接口(BCI)可用于操作这类设备。然而,在诸如家庭等现实生活环境中的日常生活活动和长期使用,需要设备具备鲁棒性以及对复杂、多变和杂乱环境的适应性,考虑到神经信号并不总是能确保安全高效使用,这可能会成为问题。本文描述了基于按需辅助的传感器共享控制(SC)方法,该方法依赖于BCI和基于深度传感器的控制的融合。所提出的辅助针对效应器的BCI远程操作,以完成满足家庭环境中移动性和操作需求的任务。通过在受控环境中进行的轮椅移动性以及抓取实验室实验,对所提出方法提供的辅助进行了评估,该实验是对一名植入名为WIMAGINE的无线64通道皮层脑电图记录植入物的四肢瘫痪患者进行的临床试验的一部分。结果表明,所提出的方法可以在这两项任务中辅助BCI用户。确实,执行任务的时间以及心理任务的变化次数都减少了。此外,还避免了诸如轮椅与环境碰撞以及可能导致物体掉落的夹爪张开等不必要的动作。所提出的方法是朝着在家中使用BCI远程操作辅助机器人迈出的步伐。确实,所提出的共享控制方法提高了这两种辅助设备的性能。临床试验注册号:NCT02550522。