a School of Life Science , Beijing Institute of Technology , Beijing , China.
b Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, The Ministry of Industry and Information Technology , Beijing Institute of Technology , Beijing , China.
Comput Assist Surg (Abingdon). 2017 Dec;22(sup1):258-266. doi: 10.1080/24699322.2017.1389404. Epub 2017 Nov 3.
EEG-based motor imagery is very useful in brain-computer interface. How to identify the imaging movement is still being researched. Electroencephalography (EEG) microstates reflect the spatial configuration of quasi-stable electrical potential topographies. Different microstates represent different brain functions. In this paper, microstate method was used to process the EEG-based motor imagery to obtain microstate. The single-trial EEG microstate sequences differences between two motor imagery tasks - imagination of left and right hand movement were investigated. The microstate parameters - duration, time coverage and occurrence per second as well as the transition probability of the microstate sequences were obtained with spatio-temporal microstate analysis. The results were shown significant differences (P < 0.05) with paired t-test between the two tasks. Then these microstate parameters were used as features and a linear support vector machine (SVM) was utilized to classify the two tasks with mean accuracy 89.17%, superior performance compared to the other methods. These indicate that the microstate can be a promising feature to improve the performance of the brain-computer interface classification.
基于脑电图的运动想象在脑机接口中非常有用。如何识别成像运动仍在研究中。脑电图（EEG）微状态反映了准稳定电位拓扑的空间配置。不同的微状态代表不同的大脑功能。在本文中，使用微状态方法对基于脑电图的运动想象进行处理，以获得微状态。研究了两种运动想象任务 - 想象左手和右手运动之间的单试 EEG 微状态序列差异。使用时空微状态分析获得微状态参数 - 持续时间，时间覆盖率和每秒发生次数以及微状态序列的转移概率。配对 t 检验显示这两个任务之间存在显着差异（P <0.05）。然后，将这些微状态参数用作特征，并使用线性支持向量机（SVM）将两个任务进行分类，平均准确率为 89.17%，优于其他方法。这表明微状态可以作为提高脑机接口分类性能的有前途的特征。
