计算机工程 ›› 2010, Vol. 36 ›› Issue (5): 182-184,.doi: 10.3969/j.issn.1000-3428.2010.05.066

• 人工智能及识别技术 • 上一篇    下一篇

基于EMD和PCA的P300分类算法

艾玲梅,李 营,马 苗   

  1. (陕西师范大学计算机科学学院,西安 710062)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2010-03-05 发布日期:2010-03-05

P300 Classification Algorithm Based on EMD and PCA

AI Ling-mei, LI Ying, MA Miao   

  1. (College of Computer Science, Shaanxi Normal University, Xi’an 710062)
  • Received:1900-01-01 Revised:1900-01-01 Online:2010-03-05 Published:2010-03-05

摘要: 提出一种基于经验模态分解(EMD)及主分量分析(PCA)的分类算法,采用支持向量机(SVM)对P300脑电信号字符拼写实验进行分类,通过EMD变换对P300脑电信号分解,从而达到去噪增强特征的效果,使用PCA方法对原始P300信号进行特征提取和集中,并送入SVM中实现分类。实验结果表明,该算法能获得高达96%的分类正确率。

关键词: 经验模态分解, P300脑电信号, 支持向量机

Abstract: A classification algorithm based on Empirical Mode Decomposition(EMD) and Principal Component Analysis(PCA) is presented, which uses Support Vector Machine(SVM) to classify the P300 ElectroEncephaloGram(EEG) signals spell experiments. It is decomposed by EMD in order to wipe off noise and strength the useful signals. The signals are extracted and focused in the help of feature scales which contain much P300 information with method of PCA. The signals are send to SVM to be classified. Experimental results show this algorithm can obtain the correct classification rate as high as 96%.

Key words: Empirical Mode Decomposition(EMD), P300 ElectroEncephaloGram(EEG) signal, Support Vector Machine(SVM)

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