Journal of South China University of Technology (Natural Science Edition) ›› 2016, Vol. 44 ›› Issue (9): 32-40.doi: 10.3969/j.issn.1000-565X.2016.09.005

• Computer Science & Technology • Previous Articles     Next Articles

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

Lü Zhao1,2 LU Yu1,2 ZHOU Beng-yan1,2 WU Xiao-pei1,2   

  1. 1.Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education,Anhui University,Hefei 230039,Anhui,China; 2.Co-Innovation Center for Information Supply and Assurance Technology,Anhui University,Hefei 230601,Anhui,China
  • Received:2015-09-16 Revised:2016-03-29 Online:2016-09-25 Published:2016-08-21
  • Contact: 吕钊( 1979-) ，男，博士，副教授，主要从事智能信息处理与人－ 机交互技术研究． E-mail:kjlz@163.com
  • About author:吕钊( 1979-) ，男，博士，副教授，主要从事智能信息处理与人－ 机交互技术研究．
  • Supported by:
    Supported by the National Natural Science Foundation of China( 61401002， 61271352) and the Natural Science Foundation of Anhui Province( 1408085QF125)

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Abstract: 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.

Key words: electrooculogram, human activity recognition, independent component analysis, saccade related independent components

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