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Computational Intelligence and Neuroscience
Volume 2007 (2007), Article ID 37695, 14 pages
Research Article

Temporal and Spatial Features of Single-Trial EEG for Brain-Computer Interface

Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received 31 December 2006; Revised 23 February 2007; Accepted 18 June 2007

Academic Editor: Fabio Babiloni

Copyright © 2007 Qibin Zhao and Liqing Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device bypassing conventional motor output pathways of nerves and muscles. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. With respect to the topographic patterns of brain rhythm modulations, the common spatial patterns (CSPs) algorithm has been proven to be very useful to produce subject-specific and discriminative spatial filters; but it didn't consider temporal structures of event-related potentials which may be very important for single-trial EEG classification. In this paper, we propose a new framework of feature extraction for classification of hand movement imagery EEG. Computer simulations on real experimental data indicate that independent residual analysis (IRA) method can provide efficient temporal features. Combining IRA features with the CSP method, we obtain the optimal spatial and temporal features with which we achieve the best classification rate. The high classification rate indicates that the proposed method is promising for an EEG-based brain-computer interface.