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.

基于脑电图的运动图像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）的微状态反映了准稳定电位地形的空间配置。不同的微状态代表不同的大脑功能。本文采用微状态方法对基于脑电图的运动图像进行处理以获得微状态。单次试验脑电图微状态序列区别了两个运动成像任务之间的差异–研究了左右手运动的想象力。通过时空微状态分析获得了微状态参数-持续时间，时间覆盖率和每秒发生的次数以及微状态序列的转移概率。结果显示出显着差异（P <0。05）在两个任务之间进行配对的t检验。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）对这两个任务进行分类，平均精度为89.17％，与其他方法相比性能更高。这些表明微状态可以是改善脑计算机接口分类性能的有前途的功能。