Liu Ye, Li Mingfen, Zhang Hao, Wang Hang, Li Junhua, Jia Jie, Wu Yi, Zhang Liqing
MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Department of Rehabilitation, Huashan Hospital, Fudan University, Shanghai 200040, China.
J Neurosci Methods. 2014 Jan 30;222:238-49. doi: 10.1016/j.jneumeth.2013.11.009. Epub 2013 Nov 23.
Stroke is one of the most common disorders among the elderly. A practical problem in stroke rehabilitation systems is that how to separate motor imagery patterns from electroencephalographic (EEG) recordings. There is a sharp decline in performance of these systems when classical algorithms, such as Common Spatial Pattern (CSP), are directly applied on stroke patients.
We propose a tensor-based scheme to detect motor imagery EEG patterns in spatial-spectral-temporal domain directly from multidimensional EEG constructed by wavelet transform method. Discriminative motor imagery EEG patterns are obtained by Fisher score strategy. Furthermore, the most contributed channel groups and frequency bands are selected from these patterns and utilized as prior knowledge for the following motor imagery tasks.
We evaluate our scheme based on EEG datasets recorded from stroke patients. The results show that our method outperforms five other traditional methods in both online and offline recognition performance.
Unlike the existing methods, motor imagery EEG patterns in spatial-spectral-temporal domain are simultaneously obtained by our method, preserving the structural information of the multi-channel time-varying EEG.
Our scheme is encouraged to be transferred to some other practical rehabilitation applications for its better performance.
中风是老年人中最常见的疾病之一。中风康复系统中的一个实际问题是如何从脑电图（EEG）记录中分离出运动想象模式。当诸如共同空间模式（CSP）等经典算法直接应用于中风患者时，这些系统的性能会急剧下降。
我们提出了一种基于张量的方案，直接从通过小波变换方法构建的多维脑电图中，在空间 - 频谱 - 时间域中检测运动想象脑电图模式。通过Fisher评分策略获得有区分性的运动想象脑电图模式。此外，从这些模式中选择最具贡献的通道组和频段，并将其用作后续运动想象任务的先验知识。
我们基于中风患者记录的脑电图数据集评估了我们的方案。结果表明，我们的方法在在线和离线识别性能方面均优于其他五种传统方法。
与现有方法不同，我们的方法同时在空间 - 频谱 - 时间域中获得运动想象脑电图模式，保留了多通道时变脑电图的结构信息。
由于我们的方案性能更好，因此鼓励将其应用于其他一些实际的康复应用中。
