P300 EEG Recognition Based on SVM Approach
LIU Hui
ZHOU Wei-dong
HUANG An-hu
摘要：In this paper, we used SVM method to detect P300 signal. Before training a classification parameter for the SVM, several preprocessing operations were applied to the data including filtering, downsampling, single trial extraction, windsorizing, electrode selection et al. With the SVM algorithm, the classification accuracy could be up to above 80%. In some cases, the accuracy could reach 100%. It is suitable to use SVM for P300 EEG recognition in the P300-based brain-computer interface (BCI) system. Our further work will include the improvement to yield higher classification accuracy using fewer trials.
机标关键词：ApproachSVMBasedEEGclassification accuracyP300interfaceyield
分类号：R318.104(医用一般科学)
论文发表日期：2009-01-01
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：5( 35-39 )
英文信息