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Source Localization for Brain-Computer Interfaces

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Brain-Computer Interfaces

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 74))

Abstract

Brain-computer interfaces provide a way to operate software without the requirement for physical movement. Electroencephalography (EEG) can be utilized to detect electrical activity in the brain during the execution of certain mental tasks, which can be used as a control signal for an interface. Automatic interpretation of BCI control signals from multichannel EEG data is generally done by application of a classification algorithm from a particular machine learning paradigm. Classification accuracy and overall BCI performance depends on a feature extraction method, which is used to represent the EEG data according to the characteristic features of a chosen BCI control signal. Certain types of control signals used in BCI can be characterized by their spatial properties. Source localization methods can be used to localize electrically active areas of the user’s brain and, hence, represent the EEG signal by its spatial features. This chapter is dedicated to the essential theory related to electromagnetic source localization problem with a particular focus on the family of sparse localization approaches. First we discuss general electromagnetic head modelling methods used to solve the EEG forward problem. Approaches to inverse problem solving, anatomical regularization and application of source localization to BCI are described later in the chapter. Finally we will discuss sparse source localization methods and present relevant simulation results.

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References

  1. Azar, A., Balas, V., Olariu, T.: Classification of EEG-Based Brain-Computer Interfaces. Advanced Intelligent Computational Technologies and Decision Support Systems, Volume 486 of Studies in Computational Intelligence, pp. 97–106. Springer, New York (2014). doi:10.1007/978-3-319-00467-9_9

  2. Baillet, S., Mosher, J., Leahy, R.: Electromagnetic brain mapping. IEEE Signal Process. Mag. 18(6), 14–30 (2001)

    Article  Google Scholar 

  3. Bishop, C.M.: Pattern Recognition and Machine Learning, Volume 4 of Information Science and Statistics. Springer, Heidelberg (2006)

    Google Scholar 

  4. Bolstad, A., Van Veen, B., Nowak, R.: Space-time event sparse penalization for magneto-/electroencephalography. NeuroImage 46(4), 1066–1081 (2009)

    Article  Google Scholar 

  5. Cotter, S., Rao, B., Engan, K.E.K., Kreutz-Delgado, K.: Sparse solutions to linear inverse problems with multiple measurement vectors. IEEE Trans. Signal Process. 53(7), 2477–2488 (2005)

    Article  MathSciNet  Google Scholar 

  6. de Munck, J.C., Peters, M.J.: A fast method to compute the potential in the multisphere model. IEEE Trans. Biomed. Eng. 40(11), 1166–1174 (1993)

    Article  Google Scholar 

  7. Ding, L., He, B.: Sparse source imaging in electroencephalography with accurate field modeling. Hum. Brain Mapp. 29(9), 1053–1067 (2008)

    Article  Google Scholar 

  8. Finke, S., Gulrajani, R.M., Gotman, J.: Conventional and reciprocal approaches to the inverse dipole localization problem of electroencephalography. IEEE Trans. Biomed. Eng. 50(6), 657–666 (2003)

    Article  Google Scholar 

  9. Grant, M., Boyd, S.: Graph implementations for nonsmooth convex programs. In: Blondel, V., Boyd, S., Kimura, H. (eds.) Recent Advances in Learning and Control, Lecture Notes in Control and Information Sciences, pp. 95–110. Springer, New York (2008)

    Google Scholar 

  10. Grant, M., Boyd, S.: CVX: Matlab software for disciplined convex programming, version 2.1 (2014)

    Google Scholar 

  11. Grech, R., Cassar, T., Muscat, J., Camilleri, K.P., Fabri, S.G., Zervakis, M., Xanthopoulos, P., Sakkalis, V., Vanrumste, B.: Review on solving the inverse problem in EEG source analysis. J. Neuroeng. Rehabil. 5(25), 1–33 (2008)

    Google Scholar 

  12. Hallez, H., Vanrumste, B., Grech, R., Muscat, J., De Clercq, W., Vergult, A., D’Asseler, Y., Camilleri, K.P., Fabri, S.G., Van Huffel, S., Lemahieu, I.: Review on solving the forward problem in EEG source analysis. J. Neuroeng. Rehabil. 4(46), 1–29 (2007)

    Google Scholar 

  13. Hämäläinen, M.S., Ilmoniemi, R.J.: Interpreting magnetic fields of the brain: minimum norm estimates. Med. Biol. Eng. Comput. 32(1), 35–42 (1994)

    Article  Google Scholar 

  14. Hawes, M., Liu, W.: Robust sparse antenna array design via compressive sensing. In: Proceedings of 18th International Conference on Digital Signal Processing, Fira, pp. 1–5, 1–3 July 2013. doi:10.1109/ICDSP.2013.6622797

  15. He, B., Musha, T., Okamoto, Y., Homma, S., Nakajima, Y., Sato, T.: Electric dipole tracing in the brain by means of the boundary element method and its accuracy. IEEE Trans. Biomed. Eng. 34(6), 406–414 (1987)

    Article  Google Scholar 

  16. Johnson, C.R.: Computational and numerical methods for bioelectric field problems. Crit. Rev. Biomed. Eng. 25(1), 1–81 (1997)

    Article  Google Scholar 

  17. Lemieux, L., McBride, A., Hand, J.W.: Calculation of electrical potentials on the surface of a realistic head model by finite differences. Phys. Med. Biol. 41(7), 1079–1091 (1996)

    Article  Google Scholar 

  18. Limpiti, T., Van Veen, B.D., Wakai, R.T.: Cortical patch basis model for spatially extended neural activity. IEEE Trans. Biomed. Eng. 53(9), 1740–1754 (2006)

    Article  Google Scholar 

  19. Liu, H., Schimpf, P.H., Dong, G., Gao, X., Yang, F., Gao, S.: Standardized shrinking LORETA-FOCUSS (SSLOFO): a new algorithm for spatio-temporal EEG source reconstruction. IEEE Trans. Biomed. Eng. 52(10), 1681–1691 (2005)

    Article  Google Scholar 

  20. Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain-computer interfaces. J. Neural Eng. 4(2), R1–R13 (2007)

    Article  Google Scholar 

  21. Mason, S.G., Bashashati, A., Fatourechi, M., Navarro, K.F., Birch, G.E.: A comprehensive survey of brain interface technology designs. Ann. Biomed. Eng. 35(2), 137–169 (2007)

    Article  Google Scholar 

  22. Meier, J.D., Aflalo, T.N., Kastner, S., Graziano, M.S.A.: Complex organization of human primary motor cortex: a high-resolution fMRI study. J. Neurophysiol. 100(4), 1800–1812 (2008)

    Article  Google Scholar 

  23. Mosher, J.C., Lewis, P.S., Leahy, R.M.: Multiple dipole modeling and localization from spatio-temporal MEG data. IEEE Trans. Biomed. Eng. 39(6), 541–557 (1992)

    Article  Google Scholar 

  24. Nicolas-Alonso, L.F., Gomez-Gil, J.: Brain Computer interfaces, a review. Sensors 12(2), 1211–1279 (2012)

    Article  Google Scholar 

  25. Olejniczak, P.: Neurophysiologic basis of EEG. J. Clin. Neurophysiol. 23(3), 186–189 (2006)

    Article  Google Scholar 

  26. Pascual-marqui, R.D.: Review of methods for solving the EEG inverse problem. Int. J. Bioelectromag. 1(1), 75–86 (1999)

    Google Scholar 

  27. Pascual-Marqui, R.D., Michel, C.M., Lehmann, D.: Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. Int. J. Psychophysiol. 18(1), 49–65 (1994)

    Article  Google Scholar 

  28. Pfurtscheller, G., Brunner, C., Schlögl, A., Lopes da Silva, F.H.: Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage 31(1), 153–159 (2006)

    Google Scholar 

  29. Pfurtscheller, G., Neuper, C.: Motor imagery and direct brain-computer communication. Proc. IEEE 89(7), 1123–1134 (2001)

    Article  Google Scholar 

  30. Salu, Y., Cohen, L.G., Rose, D., Sato, S., Kufta, C., Hallett, M.: An improved method for localizing electric brain dipoles. IEEE Trans. Biomed. Eng. 37(7), 699–705 (1990)

    Article  Google Scholar 

  31. Sanei, S., Chambers, J.: EEG Signal Processing, vol. 1. Wiley-Blackwell, New York (2007)

    Google Scholar 

  32. Schmidt, R.: Multiple emitter location and signal parameter estimation. IEEE Trans. Antennas Propag. 34(3), 276–280 (1986)

    Article  Google Scholar 

  33. Van Veen, B.D., van Drongelen, W., Yuchtman, M., Suzuki, A.: Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Trans. Biomed. Eng. 44(9), 867–880 (1997)

    Article  Google Scholar 

  34. Vanrumste, B., Van Hoey, G., Van de Walle, R., Van Hese, P., D’Have, M., Boon, P., Lemahieu, I.: The realistic versus the spherical head model in EEG dipole source analysis in the presence of noise. In: Proceedings of the 23rd Annual International Conference of the IEEE, Istanbul, vol. 1, pp. 994–997, 25–28 October 2001. doi:10.1109/IEMBS.2001.1019121

  35. Wolters, C.H., Kuhn, M., Anwander, A., Reitzinger, S.: A parallel algebraic multigrid solver for finite element method based source localization in the human brain. Comput. Vis. Sci. 5(3), 165–177 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  36. Zhang, Z.: A fast method to compute surface potentials generated by dipoles within multilayer anisotropic spheres. Phys. Med. Biol. 40(3), 335–349 (1995)

    Article  Google Scholar 

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Correspondence to Wei Liu .

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Zaitcev, A., Cook, G., Liu, W., Paley, M., Milne, E. (2015). Source Localization for Brain-Computer Interfaces. In: Hassanien, A., Azar, A. (eds) Brain-Computer Interfaces. Intelligent Systems Reference Library, vol 74. Springer, Cham. https://doi.org/10.1007/978-3-319-10978-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-10978-7_5

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