Open Access

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

  • Gary N. Garcia Molina1Email author,
  • Touradj Ebrahimi1 and
  • Jean-Marc Vesin1
EURASIP Journal on Advances in Signal Processing20032003:253269

Received: 23 April 2002

Published: 18 June 2003


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.


brain-computer interfaceEEGmultivariate signals classificationambiguity functionsimultaneous diagonalization

Authors’ Affiliations

School of Engineering, Signal Processing Institute, Swiss Federal Institute of Technology (EPFL)


© Copyright © 2003 Hindawi Publishing Corporation 2003