• Fredrik Öisjöen
Part of the Springer Theses book series (Springer Theses)


A neuron is the most fundamental cell of the brain and generates electrical activity in order to communicate with other neurons or parts of the body. Magnetoencephalography (MEG) is the measurement of the magnetic fields generated by neural activity in the brain. The corresponding technique for the electric field is electroencephalography (EEG). EEG has a long history and the first findings were reported by Richard Caton in 1875 when he measured electrical activity in the brains of rabbits and monkeys. Berger was the first to record a human EEG (he also gave the technique its name) in 1929. Since these remarkable achievements, EEG has evolved to become an important tool and is widely used for both scientific and clinical purposes. In the clinic it is particularly important for characterization of epileptic seizures.


Occipital Region Squid Magnetometer Current Dipole Spontaneous Brain Activity Squid Sensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    P.C. Hansen, M.L. Kringelbach, R. Salmelin (eds.), MEG: An Introduction to Methods (Oxford University Press, New York, 2010)Google Scholar
  2. 2.
    R. Caton, The electric currents of the brain. Br. Med. J. 2, 278 (1875)Google Scholar
  3. 3.
    H. Berger, Über das electrenphalogramm des Menschen. Arch. Physchiatr. Nervenkr. 87, 527–570 (1929)CrossRefGoogle Scholar
  4. 4.
    G.L. Barkley, C. Baumgartner, MEG and EEG in epilepsy. J. Clin. Neurophysiol. 20, 163–178 (2003)CrossRefGoogle Scholar
  5. 5.
    D. Cohen, Magnetoencephalography: Evidence of magnetic fields produced by alpha-rhythm currents. Science 161, 784–786 (1968)CrossRefGoogle Scholar
  6. 6.
    R.C. Jaklevic, J. Lambe, A.H. Silver, J.E. Mercereau, Quantum interference effects in Josephson tunneling. Phys. Rev. Lett. 12, 159–160 (1964)CrossRefGoogle Scholar
  7. 7.
    D. Cohen, Magnetoencephalography: detection of the brain’s electrical activity with a superconducting magnetometer. Science 175, 664–666 (1972)CrossRefGoogle Scholar
  8. 8.
    G.L. Romani, P. Rossini, Neuromagnetic functional localization: principles, state of the art, and perspectives. Brain Topogr. 1, 5–21 (1988)CrossRefGoogle Scholar
  9. 9.
    A.I. Ahonen, M.S. Hämäläinen, M.J. Kajola, J.E.T. Knuutila, P.P. Laine, O.V. Lounasmaa, L.T. Parkkonen, J.T. Simola, C.D. Tesche, 122-channel SQUID instrument for investigating the magnetic signals from the human brain. Phys. Scr. T49, 198–205 (1993)CrossRefGoogle Scholar
  10. 10.
    M. Hämäläinen, R. Hari, R.J. Ilmoniemi, J. Knuutila, O.V. Lounasmaa, Magnetoencephalography: theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev. Mod. Phys. 65, 413–497 (1993)CrossRefGoogle Scholar
  11. 11.
    C. del Gratta, V. Pizzella, F. Tecchio, G.L. Romani, Magnetoencephalography-a noninvasive brain imaging method with 1 ms time resolution. Rep. Prog. Phys. 64, 1759–1814 (2001)CrossRefGoogle Scholar
  12. 12.
    N.K. Logothetis, J. Pauls, M. Augath, T. Trinath, A. Oeltermann, Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001)CrossRefGoogle Scholar
  13. 13.
    M.E. Phelps, J. Hoffman, N.A. Mullani, M.M. Ter-Pogossian, Application of annihilation coincidence detection to transaxial reconstruction tomography. J. Nucl. Med. 16, 210–224 (1975)Google Scholar
  14. 14.
    K. Iramina, S. Ueno, S. Matsuoka, MEG and EEG topography of frontal midline theta rhythm and source localization. Brain Topogr. 8, 329–331 (1996)CrossRefGoogle Scholar
  15. 15.
    Y.C. Okada, A. Lahteenmäki, C. Xu, Experimental analysis of distortion of magnetoencephalography signals by the skull. Clin. Neurophysiol. 110, 230–238 (1999)CrossRefGoogle Scholar
  16. 16.
    R.M. Leahy, J.C. Moasher, M.E. Spencer, M.X. Huang, J.D. Lewine, A study of dipole localization accuracy for MEG and EEG using a human skull phantom. Electroenceph. Clin. Neurophysiol. 107, 159–173 (1998)CrossRefGoogle Scholar
  17. 17.
    M.S. Dilorio, K.Y. Yang, S. Yoshizumi, Biomagnetic measurements using low-noise integrated SQUID magnetometers operating in liquid nitrogen. Appl. Phys. Lett. 67, 1926–1928 (1995)CrossRefGoogle Scholar
  18. 18.
    Y. Zhang, Y. Tavrin, M. Mück, A.I. Braginski, C. Heiden, S. Hampson, C. Pantev, T. Elbert, Magnetoencephalography using high temperature rf SQUIDs. Brain Topogr. 5, 379–382 (1993)CrossRefGoogle Scholar
  19. 19.
    H.J. Barthelmess, M. Halverscheid, B. Schiefenhovel, E. Heim, M. Schilling, R. Zimmermann, Low-noise biomagnetic measurements with a multichannel dc-SQUID system at 77 K. IEEE. Trans. Appl. Supercond. 11, 657–660 (2001)CrossRefGoogle Scholar
  20. 20.
    D. Drung, F. Ludwig, W. Muller, U. Steinhoff, L. Trahms, H. Koch, Y.Q. Shen, M.B. Jensen, P. Vase, T. Holst, T. Freltoft, G. Curio, Integrated \(\text{ YBa}_2 \text{ Cu}_3 \text{ O}_{7-x}\) magnetometer for biomagnetic measurements. Appl. Phys. Lett. 68, 1421–1423 (1996) CrossRefGoogle Scholar
  21. 21.
    E.J. Tarte, P.E. Magnelind, A.Y. Tzalenchuk, A. Lõhmus, D.A. Ansell, M.G. Blamire, Z.G. Ivanov, R.E. Dyball, High \({T_c}\) SQUID systems for magnetophysiology. Phys. C 368, 50–54 (2002)CrossRefGoogle Scholar
  22. 22.
    P.E. Magnelind, D. Winkler, E. Hanse, E.J. Tarte, Magnetophysiology of Brain slices using an HTS SQUID magnetometer system, in Applications of Nonlinear Dynamics, ed. by V. In, P. Longhini, A. Palacios (Berlin, Understanding Complex Systems (Springer, 2009), pp. 323–330CrossRefGoogle Scholar
  23. 23.
    J.F. Stein, C.J. Stoodley, Neuroscience an Introduction (Wiley, Chichester, 2006)Google Scholar
  24. 24.
    J. Malmivuo, R. Plonsey, Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields (Oxford University Press, New York, 1995)CrossRefGoogle Scholar
  25. 25.
    J. Sarvas, Basic mathematical and electromagentic concepts of the biomagnetic inverse problem. Phys. Med. Biol. 32, 11–22 (1987)CrossRefGoogle Scholar
  26. 26.
    R. Hari, R. Salmelin, Human cortical oscillations: a neuromagnetic view through the skull. Trends Neurosci. 20, 44–49 (1997)CrossRefGoogle Scholar
  27. 27.
    W.J. Lutter, M. Maier, R.T. Wakai, Development of MEG sleep patterns and magnetic auditory evoked responses during early infancy. Clin. Neurophysiol. 117, 522–530 (2006)CrossRefGoogle Scholar
  28. 28.
    N.R. Simon, I. Mansheden, F.H. Lopes da Silva, A MEG study of sleep. Brain Res. 860, 64–76 (2000)CrossRefGoogle Scholar
  29. 29.
    E. Zamrini, F. Maestu, E. Pekkonen, M. Funke, J. Makela, M. Riley, R. Bajo, G. Sudre, A. Fernandez, N. Castellanos, F. del Pozo, C.J. Stam, B.W. van Dijk, A. Bagic, J.T. Becker, Magnetoencephalography as a putative marker for Alzheimer’s disease. Int. J. Alzheimers Dis. 280289, 2011 (2011)Google Scholar
  30. 30.
    J.L.W. Bosboom, D. Stoffers, C.J. Stam, B.W. van Dijk, J. Verbunt, H.W. Berendse, ECh. Wolters, Resting state oscillatory brain dynamics in Parkinson’s disease: an MEG study. Clin. Neurophysiol. 117, 2521–2531 (2006)CrossRefGoogle Scholar
  31. 31.
    W. Klimesch, EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res. Rev. 29, 169–195 (1999)CrossRefGoogle Scholar
  32. 32.
    S. Raghavachari, M.J. Kahana, D.S. Rizzuto, J.B. Caplan, M.P. Kirschen, B. Bourgeois, J.R. Madsen, J.E. Lisman, Gating of human theta oscillations by a working memory task. J. Neurosci. 21, 3175–3183 (2001)Google Scholar
  33. 33.
    C.D. Tesche, J. Karhu, Theta oscillations index human hippocampal activation during a working memory task. Proc. Natl. Acad. Sci. USA 97, 919–924 (1999)CrossRefGoogle Scholar
  34. 34.
    M.J. Kahana, D. Seelig, J.R. Madsen, Theta returns. Curr. Opin. Neurobiol. 11, 739–744 (2001)CrossRefGoogle Scholar
  35. 35.
    C. Ciulla, T. Takeda, H. Endo, MEG characterization of spontaneous alpha rhythm in the human brain. Brain Topogr. 11, 211–222 (1999)CrossRefGoogle Scholar
  36. 36.
    S. Salenius, M. Kajola, W.L. Thompson, S. Kosslyn, R. Hari, Reactivity of magnetic parieto-occipital alpha rhythm during visual imagery. Electroenceph. Clin. Neurophysiol. 95, 453–462 (1995)CrossRefGoogle Scholar
  37. 37.
    H. Petsche, S. Kaplan, A. von Stein, O. Filz, The possible meaning of the upper and lower alpha frequency ranges for cognitive and creative tasks. Int. J. Phsychophysiol. 26, 77–97 (1997)CrossRefGoogle Scholar
  38. 38.
    S.N. Baker, Oscillatory interactions between sensorimotor cortex and the periphery. Curr. Opin. Neurobiol. 17, 649–655 (2007)CrossRefGoogle Scholar
  39. 39.
    J.R. Hughes, Gamma, fast, and ultrafast waves of the brain: their relationships with epilepsy and behaviour. Epilepsy Beh. 13, 25–31 (2008)CrossRefGoogle Scholar
  40. 40.
    J.M. Kilner, S.N. Baker, S. Salenius, R. Hari, R.N. Lemon, Human cortical muscle coherence is directly related to specific motor parameters. J. Neurosci. 20, 8838–8845 (2000)Google Scholar
  41. 41.
    J. Mellinger, G. Schalk, C. Braun, H. Preissl, W. Rosenstiel, N. Birbaumer, A. Kübler, An MEG-based brain-computer interface (BCI). Neuroimage 36, 581–593 (2007)CrossRefGoogle Scholar
  42. 42.
    F. Oisjöen, J. F. Schneiderman, G. A. Figueras, M. L. Chukharkin, A. Kalabukhov, A. Hedström, M. Elam, and D. Winkler. High-\(T_c\) superconducting quantum interference device recordings of spontaneous brain activity: Towards high-\(T_c\) magnetoencephalography. Appl. Phys. Lett., 100(132601), 2012. 10.1063/1.3698152. Google Scholar
  43. 43.
    K. Jerbi, J.C. Mosher, S. Baillet, R.M. Leahy, On MEG modelling using multipolar expansions. Phys. Med. Biol. 47, 523–555 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fredrik Öisjöen
    • 1
  1. 1.Department of Microtechnology and Nanoscience–MC2Chalmers University of TechnologyGothenburgSweden

Personalised recommendations