A Study of BCI Signal Pattern Recognition by Using Quasi-Newton-SVM Method
YANG Chang-chun
MA Zheng-hua
SUN Yu-qiang
ZOU Ling
摘要：The recognition of electroencephalogram (EEG) signals is the key of brain computer interface (BCI).Aimed at the problem that the recognition rate of EEG by using support vector machine (SVM) is low in BCI,based on the assumption that a well-defined physiological signal which also has a smooth form"hides" inside the noisy EEG signal,a Quasi-Newton-SVM recognition method based on Quasi-Newton method and SVM algorithm was presented.Firstly,the EEG signals were preprocessed by Quasi-Newton method and got the signals which were fit for SVM.Secondly,the preprocessed signals were classified by SVM method.The present simulation results indicated the Quasi-Newton-SVM approach improved the recognition rate compared with using SVM method; we also discussed the relationship between the artificial smooth signals and the classification errors.
机标关键词：support vector machinesimulation results
分类号：R3(基础医学)
资助基金：江苏省教育科学基金(06KJD310050;06KJB520022)
论文发表日期：2006-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：7( 171-177 )
英文信息