Luo Tian-Jian, Lv Jitu, Chao Fei, Zhou Changle
Fujian Provincal Key Lab of Brain-Inspired Computing, Department of Cognitive Science, School of Informatics, Xiamen University, Xiamen, China.
Front Neurosci. 2018 Apr 5;12:219. doi: 10.3389/fnins.2018.00219. eCollection 2018.
Action observation (AO) generates event-related desynchronization (ERD) suppressions in the human brain by activating partial regions of the human mirror neuron system (hMNS). The activation of the hMNS response to AO remains controversial for several reasons. Therefore, this study investigated the activation of the hMNS response to a speed factor of AO by controlling the movement speed modes of a humanoid robot's arm movements. Since hMNS activation is reflected by ERD suppressions, electroencephalography (EEG) with BCI analysis methods for ERD suppressions were used as the recording and analysis modalities. Six healthy individuals were asked to participate in experiments comprising five different conditions. Four incremental-speed AO tasks and a motor imagery (MI) task involving imaging of the same movement were presented to the individuals. Occipital and sensorimotor regions were selected for BCI analyses. The experimental results showed that hMNS activation was higher in the occipital region but more robust in the sensorimotor region. Since the attended information impacts the activations of the hMNS during AO, the pattern of hMNS activations first rises and subsequently falls to a stable level during incremental-speed modes of AO. The discipline curves suggested that a moderate speed within a decent inter-stimulus interval (ISI) range produced the highest hMNS activations. Since a brain computer/machine interface (BCI) builds a path-way between human and computer/mahcine, the discipline curves will help to construct BCIs made by patterns of action observation (AO-BCI). Furthermore, a new method for constructing non-invasive brain machine brain interfaces (BMBIs) with moderate AO-BCI and motor imagery BCI (MI-BCI) was inspired by this paper.
动作观察（AO）通过激活人类镜像神经元系统（hMNS）的部分区域，在人脑中产生事件相关去同步化（ERD）抑制。由于多种原因，hMNS对AO反应的激活仍存在争议。因此，本研究通过控制类人机器人手臂运动的速度模式，研究了hMNS对AO速度因素反应的激活情况。由于hMNS激活通过ERD抑制反映出来，因此使用带有ERD抑制BCI分析方法的脑电图（EEG）作为记录和分析方式。六名健康个体被要求参与包含五种不同条件的实验。向个体呈现四个递增速度的AO任务和一个涉及相同运动成像的运动想象（MI）任务。选择枕叶和感觉运动区域进行BCI分析。实验结果表明，hMNS激活在枕叶区域较高，但在感觉运动区域更强烈。由于在AO过程中关注的信息会影响hMNS的激活，因此在AO递增速度模式下，hMNS激活模式先上升，随后下降至稳定水平。规律曲线表明，在合适的刺激间隔（ISI）范围内的中等速度产生最高的hMNS激活。由于脑机接口（BCI）在人与计算机/机器之间建立了一条路径，这些规律曲线将有助于构建基于动作观察模式的BCI（AO-BCI）。此外，本文启发了一种构建具有中等AO-BCI和运动想象BCI（MI-BCI）的非侵入性脑机接口（BMBI）的新方法。
