Agashe Harshavardhan A, Contreras-Vidal Jose L
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:3989-92. doi: 10.1109/EMBC.2014.6944498.
Current brain-machine interfaces (BMIs) allow upper limb amputees to position robotic arms with a high degree of accuracy, but lack the ability to control hand pre-shaping for grasping different objects. We have previously shown that low frequency (0.1-1 Hz) time domain cortical activity recorded at the scalp via electroencephalography (EEG) encodes information about grasp pre-shaping. To transfer this technology to clinical populations such as amputees, the challenge lies in constructing BMI models in the absence of overt training hand movements. Here we show that it is possible to train BMI models using observed grasping movements performed by a robotic hand attached to amputees' residual limb. Three transradial amputees controlled the grasping motion of an attached robotic hand via their EEG, following the action-observation training phase. Over multiple sessions, subjects successfully grasped the presented object (a bottle or a credit card) in 53±16 % of trials, demonstrating the validity of the BMI models. Importantly, the validation of the BMI model was through closed-loop performance, which demonstrates generalization of the model to unseen data. These results suggest `mirror neuron system' properties captured by delta band EEG that allows neural representation for action observation to be used for action control in an EEG-based BMI system.
当前的脑机接口（BMI）能让上肢截肢者高精度地定位机械臂，但缺乏控制手部预成型以抓取不同物体的能力。我们之前已经表明，通过脑电图（EEG）在头皮上记录的低频（0.1 - 1赫兹）时域皮层活动对抓握预成型的信息进行了编码。要将这项技术应用于截肢者等临床人群，挑战在于在没有明显的训练手部动作的情况下构建BMI模型。在此我们表明，利用附着在截肢者残肢上的机械手执行的观察到的抓握动作来训练BMI模型是可行的。在动作观察训练阶段之后，三名经桡骨截肢者通过他们的脑电图控制附着的机械手的抓握动作。在多个训练环节中，受试者在53±16%的试验中成功抓取了呈现的物体（一个瓶子或一张信用卡），证明了BMI模型的有效性。重要的是，BMI模型的验证是通过闭环性能进行的，这表明该模型能够推广到未见过的数据。这些结果表明，由δ波段脑电图捕获的“镜像神经元系统”特性，使得基于脑电图的BMI系统中用于动作观察的神经表征能够用于动作控制。
