P300 Classification Algorithm Based on EMD and PCA
Miao Ma · Jisuanji gongcheng · 2010
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%.