Analysis of dimension reduction by PCA and AdaBoost on spelling paradigm EEG data

Spelling Paradigm is a BCI application which aims to construct words by finding letters using P300 signals recorded via channel electrodes attached to the diverse points of the scalp. In this study effects of dimension reduction using Principal Component Analysis (PCA) and AdaBoost methods on time domain characteristics of P300 evoked potentials in Spelling Paradigm are analyzed. Support Vector Machine (SVM) is used for classification.