Efficient detection of P300 using Kernel PCA and support vector machine

P300 is one of the most studied and used event related potentials (ERP) in brain computer interfaces (BCI). The classical oddball paradigm is usually used to evoke the P300 from Electroencephalogram (EEG) signals. However, EEG raw data are noisy which make the P300 detection very difficult. In this paper, we aim to detect the P300 wave as accurate as possible using appropriate feature extraction method combined with powerful classifier. We compared four methods: Kernel principal component analysis (KPCA), Principal component analysis (PCA), Independent component analysis (ICA) and Linear discriminant analysis (LDA). Each method is used with a linear support vector machine (SVM) classifier and tested on EEG signals from three channels (FZ, CZ and PZ) of four healthy subjects. The results show that the P300 wave is efficiently detected in PZ channel using KPCA-SVM.