EURASIP Journal on Applied Signal Processing 
Volume 2003 (2003), Issue 7, Pages 713-729
doi:10.1155/S1110865703302082

Joint Time-Frequency-Space Classification of EEG in a Brain-Computer Interface Application

Gary N. Garcia Molina, Touradj Ebrahimi, and Jean-Marc Vesin

 School of Engineering, Signal Processing Institute, Swiss Federal Institute of Technology (EPFL), Lausanne CH-1015, Switzerland
Received 23 April 2002; Revised 24 February 2003

Abstract

Brain-computer interface is a growing field of interest in human-computer interaction with diverse applications ranging from medicine to entertainment. In this paper, we present a system which allows for classification of mental tasks based on a joint time-frequency-space decorrelation, in which mental tasks are measured via electroencephalogram (EEG) signals. The efficiency of this approach was evaluated by means of real-time experimentations on two subjects performing three different mental tasks. To do so, a number of protocols for visualization, as well as training with and without feedback, were also developed. Obtained results show that it is possible to obtain good classification of simple mental tasks, in view of command and control, after a relatively small amount of training, with accuracies around 80%, and in real time.