基于独立分量分析的扫视信号样本优化算法

A Sample Optimization Algorithm of Saccade Signals Based on Independent Component Analysis

为改善基于眼电图(EOG)的人体行为识别系统性能,提高多任务背景下扫视信号识别的正确率,提出了一种基于独立分量分析(ICA)的扫视信号样本优化算法.该算法首先以单次扫视数据为分析对象,根据独立成分在采集电极的映射模式,设计了一种扫视相关独立成分的自动选择方法,并建立了相应的ICA空域滤波器;然后以原始EOG线性投影后信号的识别正确率为度量准则实现对干扰扫视信号的剔除.对4类扫视信号进行了"组内测试"与"组间测试",实验结果表明,经文中算法优化后识别正确率达99.57%与98.82%,比优化前分别提升了0.57%与0.83%,说明文中算法能够对扫视信号样本进行有效的优化,提高其识别正确率.

In order to improve the performance of an electrooculogram （EOG）-based human activity recognition （HAR） system and increase the correct recognition ratio of multi-class saccade signals, a sample optimization algorithm is proposed on the basis of independent component analysis （ICA）. In the algorithm, by taking a single saccade data as the object, an automatic selection method of saccade related independent components （SRICs） is designed according to the independent components （ICs）-to-electrode mapping mode, and a corresponding ICA spatial filter is established. Then, noisy saccade samples are deleted on the basis of the correct recognition ratio of saccade signals after the linear projection of original EOGs. In the lab environment, the ICA spatial filter is utilized to classify four types of saccade signals by ＂run-to-run test＂ and ＂session-to session test＂. The results show that, in the two tests, the correct recognition ratios of the data optimized by the proposed algorithm are respectively 99.57% and 98.82% , and they are 0.57% and 0.83% higher than those of original EOG signals, which means that the proposed algorithm can effectively optimize saccade signals and thus improve the correct recognition ratio.