摘要
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.
出处
《计算机应用研究》
CSCD
北大核心
2015年第7期2101-2104,2111,共5页
Application Research of Computers
基金
国家自然科学基金资助项目(61271352)
计算智能与信号处理教育部重点实验室开放基金资助项目
安徽省教育厅自然科学基金资助项目(KJ2013B285
KJ2012Z400
KJ2012Z401
KJ2012A263)
关键词
人机交互
眼电图
实验范式
曲线拟合
支持向量机
human-computer interface
eleetrooculogram
experimental paradigm
curve fitting
support vector machine(SVM)