An Efficient Separability of Motor Imagery Using Least Square Support Vector Machine for Brain Computer Interface
Abstract
Keywords
Full Text:
PDFReferences
B. Y. Wang, X. Gao, B. Hong, C. Jia, and S. Gao, “Brain-computerinterfaces based on visual evoked potentials,” IEEE Eng. Med. Biol.Mag., vol. 27, no. 5, pp. 64–71, Sep.–Oct. 2008.
D. J. McFarland and J. R. Wolpaw, “Brain-computer interfaces forcommunication and control,” Commun. ACM, vol. 54, no. 5, pp. 60–66,2011.
G. Pfurtscheller, C. Brunner, A. Schlogl, and F. Lopes da Silva, “Murhythm (de) synchronization and EEG single-trial classification of differentmotor imagery tasks,” Neuroimage, vol. 31, no. 1, pp. 153–159,2006.
T. Kayikcioglu and O. Aydemir, “A polynomial fitting and k-NN basedapproach for improving classification ofmotor imagery BCI data,” PatternRecognit.Lett., vol. 31, pp. 1207–1215, 2010.
B.Blankertz, R.Tomioka, S.Lemm,M.Kawanable, and K. R. Muller,“Optimizing spatial filters for robust EEG single-trial analysis,” IEEE Signal Process. Mag., vol. 25, no. 1, pp. 41–56, 2008.
M.Grosse-Wentrup,C.Liefhold,K.Gramann, and M. Buss, “Beamformingin noninvasive brain-computer interfaces,” IEEE Trans. Biomed. Eng., vol. 56, no. 4, pp. 1209–1219, Apr. 2009.
W. Wu, X. Gao, B. Hong, and S. Gao, “Classifying single-trial EEGduring motor imagery by iterative spatio-spectral patterns learning(ISSPL),” IEEE Trans. Biomed. Eng., vol. 55, no. 6, pp. 1733–1743, Jun. 2008.
J. Long, Y. Li, and Z. Yu, “A semi-supervised support vector machineapproach for parameter setting in motor imagery-based brain computerinterfaces,” Cognitive Neurodynamics, vol. 4, pp. 207–216, 2010.
J. Meng, G. Liu, G. Huang, and X. Zhu, “Automated selecting subsetof channels based on CSP in motor imagery brain-computer system,”inProc. 2009 IEEE Int. Conf. Robot. Bioinformat., Guilin, China, Dec. 19–23, 2009, pp. 2290–2294.
G. M. Hieftje, R. I. Bystroff, and R. Lim, “Application of correlationanalysis for signal-to-noise enhancement in flame spectrometry: Useof correlation in determination of rhodium by atomic fluorescence,”Analytical Chem., vol. 45, no. 2, pp. 253–258, 1973.
S. Dutta, A. Chatterjee, and S. Munshi, “An automated hierarchicalgait pattern identification tool employing cross-correlation-based featureextraction and recurrent neural network based classification,” ExpertSyst., vol. 26, no. 2, pp. 202–217, 2009.
H. Esen, F. Ozgen,M. Esen, and A. Sengur, “Modelling of a new solarair heater through least-squares support vector machines,” Expert Syst.Appl., vol. 36, pp. 10673–10682, 2009.
Siuly, Y. Li, and P.Wen, “Clustering technique-based least square supportvector machine for EEG signal classification,” Comput. Method programs Biomed., vol. 104, pp. 358–372, 2011.
BCI Competition III [Online]. Available: http://www.bbci.de/competition/iii
B. Blankertz, K. R. Muller, D. J. Krusierski,G.Schalk, J. R. Wolpaw,A. Schlgl, G. Pfurtscheller, and N. Birbaumer, “The BCI competitionIII: Validating alternative approaches to actual BCI problems,” IEEETrans. Neural Syst. Rehabil. Eng., vol. 14, no. 2, pp. 153–159, Jun.2006.
S. Dutta, A. Chatterjee, and S. Munshi, “Correlation techniques anleast square support vector machine combine for frequency domainbased ECG beat classification,” Med. Eng. Phys., vol. 32, no. 10, pp.1161–1169, Dec. 2010.
S. Chandaka, A. Chatterjee, and S. Munshi, “Cross-correlation aidedsupport vector machine classifier for classification of EEG signals,”Expert Syst. Appl., vol. 36, pp. 1329–1336, 2009.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.