Sequence-based manipulation of robotic arm control in brain machine interface
Sequence-based manipulation of robotic arm control in brain machine interface
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DOI：
10.1007/s41315-018-0049-7
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发表时间：
2018-06-01
影响因子：
1.7
通讯作者：
Zhao, Xiaopeng
中科院分区：
文献类型：
--
作者：
Kilmarx, Justin;Abiri, Reza;Zhao, Xiaopeng
在脑机接口（BMI）中，通过侵入性或非侵入性方法记录大脑活动，并将其转换为命令信号以控制外部假肢设备，如计算机光标，轮椅或机器人手臂。尽管许多研究证实了BMI系统在使用侵入性方法控制多自由度（DOF）假肢设备方面的能力，使用非侵入性范例的BMI研究仍处于起步阶段。本文利用脑电信号（EEG）技术开发了一种新型的BMI机器人平台，该平台控制一个6自由度的机器人手臂。EEG信号通过无线耳机从头皮采集，并采用一种新的快速训练范式--“想象身体运动学”。采用回归模型从脑电信号中解码运动学参数。受试者被指示在多次试验中自愿控制虚拟光标，以优化的顺序击中屏幕上不同的预编程目标。在试验过程中击中目标产生的命令信号被应用于以离散方式控制机器人手臂的顺序运动，以操纵二维工作空间中的物体。这种方法源自一种基本的共享控制策略，其中机器人手臂负责根据用户的意图进行复杂的操纵。我们提出的BMI平台在基于序列的操作任务中仅经过短时间的训练（10分钟）就取得了70%的高成功率。开发的平台作为基于EEG的神经假体设备的概念验证。
In brain machine interfaces (BMI), the brain activities are recorded by invasive or noninvasive approaches and translated into command signals to control external prosthetic devices such as a computer cursor, a wheelchair, or a robotic arm. Although many studies confirmed the capability of BMI systems in controlling multi degrees-of-freedom (DOF) prosthetic devices using invasive approaches, BMI research using noninvasive paradigms is still in its infancy. In this paper, a new robotic BMI platform has been developed using electroencephalography (EEG) technology to control a 6-DOF robotic arm. EEG signals were collected from the scalp using a wireless headset exploiting a new fast-training paradigm named as "imagined body kinematics". A regression model was employed to decode the kinematic parameters from the EEG signals. The subjects were instructed to voluntarily control a virtual cursor in multiple trials to hit different pre-programmed targets on a screen in an optimized sequence. The command signals generated from hitting the targets during trials were applied to control sequential movements of the robotic arm in a discrete manner to manipulate an object in a two-dimensional workspace. This approach is derived from a basic shared control strategy where the robotic arm is responsible for carrying out complex maneuvers based on the user's intention. Our proposed BMI platform yielded a high success rate of 70% in a sequence-based manipulation task after only a short time of training (10 min). The developed platform serves as a proof-of-concept for EEG-based neuro-prosthetic devices.