Motor imagery classification based on joint regression model and spectral power
Motor imagery classification based on joint regression model and spectral power
复制标题
基于联合回归模型和谱功率的运动想象分类
DOI：
10.1007/s00521-012-1244-3
复制
发表时间：
2013-12-01
影响因子：
6
通讯作者：
Kong, Wanzeng
中科院分区：
文献类型：
--
作者：
Hu, Sanqing;Tian, Qiangqiang;Kong, Wanzeng
基于运动想象的脑机接口（BCI）通过对不同想象任务（如手部动作）的脑电模式进行分类，将受试者的运动意图转化为控制信号。自回归（AR）模型是描述运动想象模式的常用方法之一，被研究者广泛用于研究被试的运动意图。本文利用联合回归（JR）模型，提出了一种将JR模型系数与两个特定频率处的谱功率相结合的算法来对不同的MI模式进行分类。该算法在来自BCI 2003数据集III的一个主题的训练数据上产生90%的分类准确度，在测试数据上产生80%的分类准确度。结果表明，该模型的预测效果优于AR模型。我们还将该算法应用于实验室的一个受试者的MI任务，在测试数据上的分类准确率达到97.86%。结果表明，JR模型和谱功率相结合，可以达到更高的精度分类MI任务。
A brain-computer interface (BCI) based on motor imagery (MI) translates the subject's motor intention into a control signal through classifying electroencephalogram (EEG) patterns of different imagination tasks, for example, hand movements. Auto-regression (AR) model is one of the popular methods to describe motor imagery patterns, which is widely used by researchers to resolve subject's motor intention. In this paper, we use joint regression (JR) model and propose an algorithm by combining the coefficients of JR model and spectral powers at two specific frequencies to classify different MI patterns. The algorithm produces a classification accuracy of 90 % on the training data of one subject from BCI2003 Data set III and 80 % on the test data. The results are better than that by using AR model. We also apply the algorithm to MI tasks of one subject in our laboratory, and the classification accuracy can reach 97.86 % on the test data. The results demonstrate that the combination of JR model and spectral powers can achieve much higher accuracy for classification of MI tasks.