School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, 710121 Xi'an, Shaanxi, China.
Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, 710121 Xi'an, Shaanxi, China.
J Integr Neurosci. 2021 Jun 30;20(2):411-417. doi: 10.31083/j.jin2002042.
In this paper, the differences between two motor imagery tasks are captured through microstate parameters (occurrence, duration and coverage, and mean spatial correlation (Mspatcorr)) derived from a novel method based on electroencephalogram microstate and Teager energy operator. The results show that the significance between microstate parameters for two tasks is different ( < 0.05) with paired -test. Furthermore, these microstate parameters are utilized as features. Support vector machine is utilized to classify the two tasks with a mean accuracy of 93.93%, which yielded superior performance compared to the other methods.
本文通过基于脑电微状态和 Teager 能量算子的新方法得出的微状态参数（出现率、持续时间和覆盖率以及平均空间相关性（Mspatcorr））捕捉到两种运动想象任务之间的差异。结果表明，配对检验表明两种任务的微状态参数之间存在显著差异（<0.05）。此外，这些微状态参数可用作特征。支持向量机用于对两种任务进行分类，平均准确率为 93.93%，与其他方法相比表现更优。
