Brain-Computer Interface Binary Classification using Regularized CSP and Stacked Concept

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2016 by IJETT Journal
Volume-38 Number-5
Year of Publication : 2016
Authors : B. Bijitha, Nandakumar Paramparambath
DOI :  10.14445/22315381/IJETT-V38P249

Citation 

B. Bijitha, Nandakumar Paramparambath"Brain-Computer Interface Binary Classification using Regularized CSP and Stacked Concepte", International Journal of Engineering Trends and Technology (IJETT), V38(5),271-275 August 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Brain-Computer Interface technology is the one in which the brain signals acquired from scalp recordings are used to control external devices like artificial limbs, computers, etc. Even though studies on BCI technology are progressing, a consistent algorithm that will work with all types of data and environment are not developed so far. In this paper, an algorithm with feature extraction using regularized version of CSP and PCA, then the features are classified using the stacked concept classifier. The algorithm is evaluated using kappa coefficient and compared with existing algorithms.

 References

[1] World Report on Disability 2011 from World Health Organization and The World Bank available at http://www .who.int/disabilities/world_report/2011/report.pdf
[2] Jonathan R. Wolpaw, ?Brain-Computer Interface Research Comes of Age: Traditional Assumptions Meet Emerging Realities, Journal of Motor Behavior, Vol. 42, No. 6, pp. 351-353, 2010.
[3] Jonathan R. Wolpaw, Niels Birbaumer, Dennis J. McFarland, Gert Pfurtscheller, Theresa M. Vaughan, ?Brain-computer interfaces for communication and control, Elsevier Science Ireland Ltd, Clinical Neurophysiology 113, pp. 767–791, 2002.
[4] Cincotti. F, Mattia. D, Aloise. F, Bufalari. S, Schalk. G, Oriolo. G, Cherubini. A, Marciani. M.G, Babiloni. F, ?Noninvasive brain-computer interface system: Towards its application as assistive technology, Robotics and Neuroscience, Vol. 75, pp. 796-803, April 2008.
[5] Roman. K, Benjamin. B, Gabriel. C, Klaus-Robert. M, ?The Berlin Brain-Computer Interface (BBCI)- Towards a new communication channel for online control in gaming applications, Multimedia Tools and Applications 33, pp. 73–90, 2007.
[6] Aboul Ella Hussanien, and Ahmed Taher Azar,? Brain Computer Interface Current Trends and Applications, Intelligent Systems Reference Library, Vol. 74, Springer Interational Publishing, 2015.
[7] Obed Carrera-Leon, Juan Manuel Ramirez, Vicente Alarcon-Aquino, Mary Baker, David DCroz-Baron, Pilar Gomez-Gil, ?A Motor Imagery BCI Experiment using Wavelet Analysis and Spatial Patterns Feature Extraction, IEEE Conference on Engineering Applications (WEA), 2012 Workshop on, 2012.
[8] Katarzyna Blinowska, Piotr Durka, ?Electroencephalography (EEG), Wiley Encyclopedia of Biomedical Engineering, John Wiley & Sons, Inc., 2006.
[9] Yijun Wang, Xiaorong Gao, Bo Hong, Chuan Jia, and Shangkai Gao, ?Brain-Computer Interfaces Based on Visual Evoked Potentials Feasibility of Practical System Designs, IEEE Engineering in Medicine and Biology Magazine, pp.64-71, September/October, 2008.
[10] J. R. Wolpaw, D. J. Mcfarland, and T. M. Vaughan, ?Brain– Computer Interface Research at The Wadsworth Center, IEEE Transactions on Rehabilitation Engineering, Vol. 8, No.2, pp.222-226, June 2000.
[11] Sun-Yuge, Ye-Ning, Zhao-Lihong, Xu-Xinh, ?Research on Feature Extraction Algorithms in BCI”, IEEE, Chinese Control and Decision Conference ( CCDC), pp.5874-587, 2009.
[12] Dennis J. McFarland, Dean J. Krusienski and Jonathan R. Wolpaw, ?Brain computer interface signal processing at the Wadsworth Center: mu and sensorimotor beta rhythms, Progress in Brain Research, Vol. 159, pp.411-419, February 2006.
[13] P.Shenoy, M.Krauledat, B.Blankertz, R.P.N.Rao, and K.R.Müller, ?Towards Adaptive Classification for BCI, Journal of Neural Engineering, Vol. 3, pp. R13, March 2006.
[14] M. Tangermann et al., ?Review of the BCI competition IV, Frontal Neuroscience, Vol. 6, pp. 55, July 2012. [15] L.F.Nicolas-Alonso and J.Gomez Gil,?Brain computer interfaces, a review, Sensors, vol.12, pp.1211–1279, Jan.2012.
[16] K.K.Ang, Z.Y.Chin, C.Wang, C.Guan, and H.Zhang, ?Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b, Frontal Neuroscience, Vol. 6, March 2012.
[17] Luis F.Nicolas-Alonso, Rebeca Corralejo, Javier Gomez- Pilar, Daniel Álvarez ,and Roberto Hornero, ?Adaptive Stacked Generalization for Multiclass Motor Imagery-Based Brain Computer Interfaces, IEEE Transactions on Neural Systems and Rehabilitation Engineering,Vol.23, No.4, pp. 702-712, July2015
[18] Wojciech Samek, Motoaki Kawanabe, and Klaus-Robert Muller , ?Divergence-Based Framework for Common Spatial Patterns Algorithms, IEEE Reviews in Biomedical Engineering, Vol. 7, pp.50-72, 2014.
[19] B. Blankertz, R.Tomioka, S.L.M.Kawanabe, and K.R.Muller, ?Optimizing Spatial Filters for Robust EEG Single-Trial Analysis, IEEE Signal Processing Magazine, Vol. 25, pp. 41–56, January 2008.
[20] Haiping Lu, How-Lung Eng, Cuntai Guan, Konstantinos N. Plataniotis, and Anastasios N. Venetsanopoulos, ?Regularized Common Spatial Pattern with Aggregation for EEG Classification in Small-Sample Setting, IEEE Transactions on Biomedical Engineering, Vol. 57, No. 12, pp.2936-2946, December 2010.
[21] Fabien Lotte, and Cuntai Guan, “Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms”, IEEE Transactions on Biomedical Engineering, Vol. 58, No. 2, pp.355-362, February 2011.
[22] Li Ke, Rui Li, ?Classification of EEG Signals by Multi- Scale Filtering and PCA, IEEE, pp. 362-366, 2009.
[23] C.Vidaurre, M.Kawanabe, P.vonBunau, B.Blankertz, and K.Muller, ?Toward Unsupervised Adaptation of LDA for brain–computer interfaces, IEEE Transaction on Biomedical Engineering, Vol.58, pp.587–597, March 2011.
[24] Z. Jinyin, G. Sudre, L. Xin, W. Wei, D. J. Weber, and A. Bagic, ?Clustering Linear Discriminant Analysis for MEGBased Brain Computer Interfaces, IEEE Transaction on Neural System and Rehabilitation Engineering, Vol.19, pp. 221–231, March 2011.
[25] D. Cai, X. He, and J. Han, ?SRDA: An efficient Algorithm for Large Scale Discriminant Analysis, IEEE Transaction on Knowledge Data Engineering, Vol. 20, pp. 1–12, January 2008.

Keywords
brain computer interfaces, EEG, feature extraction, common spatial pattern, classification, regularized linear discriminant analysis.