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Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India (CSI) Volume 1 pp 71–82Cite as

Offline Detection of P300 in BCI Speller Systems

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 337))

Abstract

The paper presents a framework for offline analysis of P300 speller system using seeded k-means based ensemble SVM. Due to the use of small-datasets for the training of classifier, the performance deteriorates. The Proposed framework emphases on semi-supervised clustering approach for training the SVM classifier with large amount of data. The normalized mutual information (NMI) has used for cluster validation that gives maximum 88 clusters on 10 fold cross-validation dataset with NMI approx equals to 1. The proposed framework has applied to the EEG data acquired from two subjects and provided by the Wadsworth center for brain-computer interface (BCI) competition III. The experimental results show the increase in SNR value and obtain better accuracy results than linear, polynomial or rbf kernel SVM.

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References

  1. Polich, J.: Neuropsychology of P300. In: Luck, S.J., Kappenman, E.S. (eds.) Handbook of Event-related Potential Components. Oxford University Press (2010)

    Google Scholar 

  2. Vahabi, Z., Amirfattahi, R., Mirzaei, A.R.: Enhancing P300 Wave of BCI Systems Via Negentropy in Adaptive Wavelet Denoising. Journal of Medical Signals & Sensors 1(3), 165–176 (2011)

    Google Scholar 

  3. Kaper, M.: P300 based brain computer interfacing. Thesis, pp. 1–149. Bielefeld University (2006)

    Google Scholar 

  4. Salvaris, M., Sepulveda, F.: Wavelets and Ensemble of FLDs for P300 Classification. In: Proceedings of the 4th International IEEE EMBS Conference on Neural Engineering, Antalya, Turkey, April 29-May 2 (2009)

    Google Scholar 

  5. Finke, A., Lenhardt, A., Ritter, H.: The Mind Game: A P300-based Brain Computer Interface Game. Neural Networks 22(9), 1329–1333 (2009)

    Article  Google Scholar 

  6. https://www.bhsu.edu/Portals/91/InstrumentalAnalysis/StudyHelp/LectureNotes/Chapter5.pdf

  7. Kaur, M., et al.: Semi-supervised Clustering Approach for P300 based BCI Speller Systems. In: Accepted for Publication in Fifth International Conference on Recent Trends in Information, Telecommunication and Computing, ITC 2014, will be Held During, Chandigarh, March 21-22 (2014)

    Google Scholar 

  8. El Dabbagh, H., Fakhr, W.: Multiple classification algorithms for the BCI P300 speller diagram using an ensemble of SVMs. In: IEEE Conference and Exhibition (GCC), Dubai, United Arab Emirates, February 19-22 (2011)

    Google Scholar 

  9. http://bbci.de/competition/iii/#datasets

  10. Espinosa, R.: Increased Signal to Noise Ratio in P300 Potentials by the Method of Coherent Self-Averaging in BCI Systems. World Academy of Science, Engineering and Technology, International Journal of Medical, Pharmaceutical Science and Engineering 7(11), 106–110 (2013)

    Google Scholar 

  11. Espinosa, R.: Increased Signal to Noise Ratio in P300 Potentials by the Method of Coherent Self-Averaging in BCI Systems. World Academy of Science, Engineering and Technology, International Journal of Medical, Pharmaceutical Science and Engineering 7(11), 106–110 (2013)

    Google Scholar 

  12. Gerardo, R.-C., et al.: On signal P-300 detection for BCI applications based on Wavelet analysis and ICA Preprocessing. In: Electronics, Robotics and Automotive Mechanics Conference, September 28 -October 1, pp. 360–365 (2010) ISBN: 978-1-4244-8149-1

    Google Scholar 

  13. Presentation by Ata Kaban, Independent Component Analysis & Blind Source Separation. The University of Birmingham

    Google Scholar 

  14. Cristea, P.: Independent Component Analysis (ICA) for Genetic Signals. In: SPIE’s International Symposium, San Jose Convention Center, San Jose, California, USA, January 20-26, pp. 20–26 (2001)

    Google Scholar 

  15. Hyvärinen, A., Oja, E.: Independent Component Analysis: A Tutorial. In: Neural Networks, vol. 13, pp. 411–430. Helsinki University of Technology, Laboratory of Computer and Information Science, Espoo (2000)

    Google Scholar 

  16. Srinivasulu, A., Sreenath Reddy, M.: Artifacts Removing From EEG Signals by ICA Algorithms. IOSR Journal of Electrical and Electronics Engineering (IOSRJEEE) 2(4), 11–16 (2012) ISSN: 2278-1676

    Google Scholar 

  17. Schimpf, P.H., Liu, H.: Localizing Sources of the P300 using ICA, SSLOFO, and Latency Mapping. Journal of Biomechanics, Biomedical and Biophysical Engineering 2(1), 1–11 (2008)

    Google Scholar 

  18. Naik, G.R., Kumar, D.K.: An Overview of Independent Component Analysis and Its Applications. Informatica 35, 63–81 (2011)

    MATH  Google Scholar 

  19. Lemm, S., et al.: Enhancing the Signal-to-Noise Ratio of ICA-Based Extracted ERPs. IEEE Transactions on Biomedical Engineering 53(4), 601–607 (2006)

    Article  Google Scholar 

  20. Raj, A., Deo, A., Bandil, M.K.: Canvassing Various Techniques for Removal of Biological Artifact in EEG. International Journal of Recent Technology and Engineering (IJRTE) 1(3), 149–154 (2012) ISSN: 2277-3878

    Google Scholar 

  21. Aviyente, S., et al.: Analysis of Event Related Potentials Using Pca and Matching Pursuit on the Time-Frequency Plane. In: IEEE Conference Proceesing on Med. Biol. Soc. (2006)

    Google Scholar 

  22. Kaur, M., Ahmed, P., Qasim Rafiq, M.: Analysis of Extracting Distinct Functional Components of P300 using Wavelet Transform. In: Proceedings of 4th International Conference on Mathematical Models for Engineering Science (MMES 2013) and the 2nd International Conference on Computers, Digital Communications and Computing (ICDCC 2013), Brasov, Romania, June 1-3, pp. 57–62 (2013) ISBN: 978-1-61804-194-4

    Google Scholar 

  23. Khatwani, P., Tiwari, A.: A survey on different noise removal techniques of EEG signals. International Journal of Advanced Research in Computer and Communication Engineering 2(2), 1091–1095 (2013)

    Google Scholar 

  24. Bair, E.: Semi-supervised clustering methods. 1270 © 2013 Wiley Periodicals, Inc. (2013), doi:10.1002/wics (Article first published online: July 23, 2013)

    Google Scholar 

  25. Boswell, D.: Introduction to Support Vector Machines, http://www.work.caltech.edu/boswell/IntroToSVM.pdf (August 6, 2002)

  26. Moore Andrew, W.: Professor School of Computer Science. Lecture Notes on Support Vector Machines. Carnegie Mellon University (November 23, 2001), http://www.cs.cmu.edu/~cga/ai-course/svm.pdf

  27. Matthias, K.: BCI Competition 2003—Data Set IIb: Support Vector Machines for the P300 Speller Paradigm. IEEE Transactions on Biomedical Engineering 51(6), 1073–1076 (2004)

    Article  Google Scholar 

  28. Daniel, M.: Classification and Clustering using SVM. 2nd Ph.D Report, Thesis Title: Data Mining for Unstructured Data, University of Sibiu (2005)

    Google Scholar 

  29. Xia, X.-L., Lyu, M.R., Lok, T.-M., Huang, G.-B.: Methods of Decreasing the Number of Support Vectors via k -Mean Clustering. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 717–726. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  30. Sugato, B.: Semi-supervised Clustering: Learning with Limited User Feedback. PhD Proposal, The University of Texas at Austin (November 2003) (Also Appears as Technical Report, UT-AI-TR-03-307)

    Google Scholar 

  31. Sugato, B.: Semi-supervised Clustering: Probabilistic Models, Algorithms and Experiments. Thesis, The University of Texas at Austin (August 2005)

    Google Scholar 

  32. Sugato, B., et al.: Semi-supervised Clustering by Seeding. In: Proceedings of the 19th International Conference on Machine Learning (ICML 2002), Sydney, Australia, pp. 19–26 (July 2002)

    Google Scholar 

  33. Sugato, B., et al.: A Probabilistic Framework for SemiSupervised Clustering. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2004), Seattle, WA, pp. 59–68 (August 2004)

    Google Scholar 

  34. Wang, J.: An Effective Semi-Supervised Clustering Framework Integrating Pairwise Constraints and Attribute Preferences. Computing and Informatics 31, 597–612 (2012)

    Google Scholar 

  35. Tang, W., et al.: Enhancing Semi-Supervised Clustering: A Feature Projection Perspective. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 707–716. ACM (2007)

    Google Scholar 

  36. Mandeep Kaur, P., Ahmed, A.K., Soni, M.: Qasim Rafiq, Wavelet Transform use for P300 Signal Clustering by Self-Organizing Map. International Journal of Scientific & Engineering Research 4(11) (November 2013)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Science & Engg., Lingaya’s University, Faridabad, India

    Mandeep Kaur

  2. School of Computer Science & Engg., Sharda University, Greater Noida, U.P., India

    A. K. Soni

  3. Dept. Computer Sc. & Engg., AMU, Aligarh, India

    M. Qasim Rafiq

Authors
  1. Mandeep Kaur
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  2. A. K. Soni
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  3. M. Qasim Rafiq
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Corresponding author

Correspondence to Mandeep Kaur .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, India

    Suresh Chandra Satapathy

  2. School of Information Technology, Jawaharlal Nehru Technological University Hyderabad, Hyderabad, India

    A. Govardhan

  3. Department of CSE, CMR Technical Campus, Hyderabad, India

    K. Srujan Raju

  4. Department of Computer Science & Engineering, Faculty of Engg., Tech. & Management, University of Kalyani, Kalyani, West Bengal, India

    J. K. Mandal

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Kaur, M., Soni, A.K., Rafiq, M.Q. (2015). Offline Detection of P300 in BCI Speller Systems. In: Satapathy, S., Govardhan, A., Raju, K., Mandal, J. (eds) Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India (CSI) Volume 1. Advances in Intelligent Systems and Computing, vol 337. Springer, Cham. https://doi.org/10.1007/978-3-319-13728-5_8

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  • DOI: https://doi.org/10.1007/978-3-319-13728-5_8

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13727-8

  • Online ISBN: 978-3-319-13728-5

  • eBook Packages: EngineeringEngineering (R0)

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