Compared with the efficacy of traditional physical therapy, a new therapy utilizing motor imagery can induce brain plasticity and allows partial recovery of motor ability in patients with hemiplegia after stroke. Here, we proposed an updated paradigm utilizing motor coordination imagery involving the lower limbs (normal gait imagery and hemiplegic gait imagery after stroke) and decoded such imagery via an electroencephalogram- (EEG-) based brain network. Thirty subjects were recruited to collect EEGs during motor coordination imagery involving the lower limbs. Time-domain analysis, power spectrum analysis, time-frequency analysis, brain network analysis, and statistical analysis were used to explore the neural mechanisms of motor coordination imagery involving the lower limbs. Then, EEG-based brain network features were extracted, and a support vector machine was used for decoding. The results showed that the two employed motor coordination imageries mainly activated sensorimotor areas; the frequency band power was mainly concentrated within theta and alpha bands, and brain functional connections mainly occurred in the right forehead. The combination of the network attributes of the EEG-based brain network and the spatial features of the adjacency matrix had good separability for the two kinds of gait imagery ( < 0.05), and the average classification accuracy of the combination feature was 92.96% ± 7.54%. Taken together, our findings suggest that brain network features can be used to identify normal gait imagery and hemiplegic gait imagery after stroke.

中文翻译：

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通过基于 EEG 的脑网络解码涉及下肢的运动协调图像

与传统物理疗法的疗效相比，一种利用运动想象的新疗法可以诱导脑可塑性，并使中风后偏瘫患者的运动能力部分恢复。在这里，我们提出了一种更新的范式，利用涉及下肢的运动协调图像（正常步态图像和中风后偏瘫步态图像），并通过基于脑电图（EEG-）的大脑网络解码此类图像。招募了 30 名受试者在涉及下肢的运动协调图像期间收集脑电图。时域分析、功率谱分析、时频分析、脑网络分析和统计分析被用来探索涉及下肢的运动协调意象的神经机制。然后，提取基于脑电图的大脑网络特征，并使用支持向量机进行解码。结果表明，两种使用的运动协调意象主要激活感觉运动区；频段功率主要集中在theta和alpha频段，大脑功能连接主要发生在右前额。基于EEG的大脑网络的网络属性和邻接矩阵的空间特征的结合对两种步态图像具有良好的可分离性（ < 0.05），组合特征的平均分类准确率为92.96%±7.54%。综上所述，我们的研究结果表明，大脑网络特征可用于识别中风后的正常步态图像和偏瘫步态图像。