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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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
Baillet, S., Mosher, J., Leahy, R.: Electromagnetic brain mapping. IEEE Signal Process. Mag. 18(6), 14–30 (2001)
Bishop, C.M.: Pattern Recognition and Machine Learning, Volume 4 of Information Science and Statistics. Springer, Heidelberg (2006)
Bolstad, A., Van Veen, B., Nowak, R.: Space-time event sparse penalization for magneto-/electroencephalography. NeuroImage 46(4), 1066–1081 (2009)
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)
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)
Ding, L., He, B.: Sparse source imaging in electroencephalography with accurate field modeling. Hum. Brain Mapp. 29(9), 1053–1067 (2008)
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)
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)
Grant, M., Boyd, S.: CVX: Matlab software for disciplined convex programming, version 2.1 (2014)
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)
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)
Hämäläinen, M.S., Ilmoniemi, R.J.: Interpreting magnetic fields of the brain: minimum norm estimates. Med. Biol. Eng. Comput. 32(1), 35–42 (1994)
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
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)
Johnson, C.R.: Computational and numerical methods for bioelectric field problems. Crit. Rev. Biomed. Eng. 25(1), 1–81 (1997)
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)
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)
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)
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)
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)
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)
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)
Nicolas-Alonso, L.F., Gomez-Gil, J.: Brain Computer interfaces, a review. Sensors 12(2), 1211–1279 (2012)
Olejniczak, P.: Neurophysiologic basis of EEG. J. Clin. Neurophysiol. 23(3), 186–189 (2006)
Pascual-marqui, R.D.: Review of methods for solving the EEG inverse problem. Int. J. Bioelectromag. 1(1), 75–86 (1999)
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)
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)
Pfurtscheller, G., Neuper, C.: Motor imagery and direct brain-computer communication. Proc. IEEE 89(7), 1123–1134 (2001)
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)
Sanei, S., Chambers, J.: EEG Signal Processing, vol. 1. Wiley-Blackwell, New York (2007)
Schmidt, R.: Multiple emitter location and signal parameter estimation. IEEE Trans. Antennas Propag. 34(3), 276–280 (1986)
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)
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
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)
Zhang, Z.: A fast method to compute surface potentials generated by dipoles within multilayer anisotropic spheres. Phys. Med. Biol. 40(3), 335–349 (1995)
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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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