Browse > Article
http://dx.doi.org/10.5302/J.ICROS.2013.13.1930

Parallel Model Feature Extraction to Improve Performance of a BCI System  

Chum, Pharino (School of Electrical and Electronics Engineering, Chung-Ang University)
Park, Seung-Min (School of Electrical and Electronics Engineering, Chung-Ang University)
Sim, Kwee-Bo (School of Electrical and Electronics Engineering, Chung-Ang University)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.19, no.11, 2013 , pp. 1022-1028 More about this Journal
Abstract
It is well knowns that based on the CSP (Common Spatial Pattern) algorithm, the linear projection of an EEG (Electroencephalography) signal can be made to spaces that optimize the discriminant between two patterns. Sharing disadvantages from linear time invariant systems, CSP suffers from the non-stationary nature of EEGs causing the performance of the classification in a BCI (Brain-Computer Interface) system to drop significantly when comparing the training data and test data. The author has suggested a simple idea based on the parallel model of CSP filters to improve the performance of BCI systems. The model was tested with a simple CSP algorithm (without any elaborate regularizing methods) and a perceptron learning algorithm as a classifier to determine the improvement of the system. The simulation showed that the parallel model could improve classification performance by over 10% compared to conventional CSP methods.
Keywords
parallel model; common spatial pattern; perceptron learning algorithm; electroencephalography; brain-computer interface;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 J. Sim and C. Wright, "The Kappa statistic in realizability studies: use, interpretation, and sample size requirements," Physical Therapy, vol. 85, pp. 257-268, 2005.
2 G. Pfurtscheller and C. Neuper, "Motor imagery and direct brain-computer communication," Proc. of the IEEE, vol. 89, pp. 1123-1134, 2001.   DOI   ScienceOn
3 B. Blankertz and R. Tomioka, "Optimizing spatial filters for robust EEG single-trial analysis," IEEE Signal Processing Magazine, vol. 25, pp. 41-56, 2008.
4 F. Lotte and C. Guan, "Regularizing common spatial patterns to improve BCI designs: unified theory and new Algorithm," IEEE Transaction on Bio-medical Engineering, vol. 58, no. 2, pp. 255-362, 2011.
5 G. Pfurtscheller, C. Brunner, A. Schlogl, and F. H. "Lopes da Silva, Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks," NeuroImage, vol. 31, pp. 153-159, 2006.   DOI   ScienceOn
6 G. Dornhenge, B. Blankertz, G. Curio, and K. Muller, "Boosting bit rates in noninvasive EEG single-trail classification by feature combination and multiclass paradigm," IEEE Transaction on Bio-Medical Engineering, vol. 51, pp. 992-1002, 2004.
7 Y. H. Kim, K. E. Ko, S. M. Park, and K. B. Sim, "Practical use technology for robot control in BCI environment based on motor imagery-P300," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 19, no. 3, pp. 227-232, 2013.   과학기술학회마을   DOI   ScienceOn
8 K. E. Ko and K. B. Sim, "HSA-based HMM optimization method for analyzing EEG pattern of motor imagery," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 17, no. 8, pp. 747-752, 2011.   과학기술학회마을   DOI   ScienceOn