Computational Intelligence and Neuroscience
Volume 2007 (2007), Article ID 37695, 14 pages
doi:10.1155/2007/37695
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
Recommended by Fabio Babiloni
Abstract
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.