Analysis of Dimension Reduction by PCA and AdaBoost on Spelling Paradigm EEG Data


6th International Conference on Biomedical Engineering and Informatics (BMEI), Hangzhou, Çin, 16 - 18 Aralık 2013, ss.192-196 identifier identifier

  • Doi Numarası: 10.1109/bmei.2013.6746932
  • Basıldığı Şehir: Hangzhou
  • Basıldığı Ülke: Çin
  • Sayfa Sayıları: ss.192-196


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.