基于曲线拟合和SVM的眼动信号分类算法研究

Research on classification algorithm of eye movements signals based on curve fitting and SVM

为提高基于EOG的眼动信号分类算法精度,改进基于EOG的人—机交互系统性能,提出了一种基于曲线拟合(curve fitting)与支持向量机(SVM)的眼动信号分类算法(CF-SVM),并设计了新的实验范式,增加了"扫视保持"环节。该算法采用曲线拟合方法进行特征提取,在此基础上,使用SVM分类器对眼动信号进行分类。实验室环境下,对9名眼部活动正常的受试者进行了眼动数据采集与识别,CF-SVM算法的平均分类准确率达到98.3%,与其他几种眼动识别方法相比较,其平均正确率分别提升了9.4%、5.9%、1.0%。实验结果表明,CF-SVM算法在眼动信号识别中表现了良好的性能,具有高的分类精度和鲁棒性。

To improve the classification accuracy of eye movement signals and the performance of the BCI system based on EOG signals, this paper proposed a classification algorithm of eye movement signals based on curve fitting and support vector machine （ CF-SVM）, and increased a new experimental paradigm named ＂saccades keeping＂. It applied a curve fitting method to extract feature parameters of eye movement signals, on this basis, classified the EOG signals using SVM classifier. In laboratory environment, the experimental data contained eye movements of 9 normal subjects, and the average classification accuracy of CF-SVM algorithm achieved 98.3%, increased 9.4%, 5.9% and 1.0% respectively compared with other identification methods. Experimental results reveal that the CF-SVM algorithm has high classification accuracy and good robustness in EOG signal identification.