Computer Vision And Pattern Analysis Laboratory Home Page  Home
People  People
Publications  Publications
Publications  Databases
Contact Information  Contact
Supported Research Projects  Supported Research Projects
Research Activites  Research Activites
Research Groups
SPIS - Signal Processing and Information Systems Lab.SPIS - Signal Processing and Information Systems Lab.
Medical Vision and Analysis Group  Medical Research Activities
Biometrics Research Group  Biometrics Research Group
SPIS - Signal Processing and Information Systems Lab.MISAM - Machine Intelligence for Speech Audio and Multimedia.
Knowledge Base
  Paper Library
Factors that affect classification performance in EEG based brain-computer interfaces
Authors: Argunşah, Ali Özgür and Çürüklü, Ali Baran and Çetin, Müjdat and Erçil, Aytül
Published in: SIU2007 - Signal Processing and Communications Applications, 2007. SIU 2007. IEEE 15th
Publication year: 2007
Abstract: In this paper, some of the factors that affect classification performance of EEG based Brain-Computer Interfaces (BCI) is studied. Study is specified on P300 speller system which is also an EEG based BCI system. P300 is a physiological signal that represents a response of brain to a given stimulus which occurs right 300ms after the stimulus onset. When this signal occurs, it changes the continuous EEG some micro volts. Since this is not a very distinguished change, some other physiological signals (movement of muscles and heart, blinking or other neural activities) may distort this signal. In order to understand if there is really a P300 component in the signal, consecutive P300 epochs are averaged over trials. In this study, we have been tried two different multi channel data handling methods with two different frequency windows. Resulted data have been classified using Support Vector Machines (SVM). It has been shown that proposed method has a better classification performance.
  download full paper

Home Back