Brain Topography

, Volume 8, Issue 4, pp 385–396 | Cite as

A correlation study of averaged and single trial MEG signals: The average describes multiple histories each in a different set of single trials

  • Lichan Liu
  • Andreas A. Ioannides


Our understanding of the link between electrical events in the brain and behaviour is based on indirect measures. Positron Emission Tomography (PET) and functional Magnetic Resonance Imaging (fMRI) rely on haemodynamic processes which are slower by two to three orders of magnitude than the processes characterizing normal and pathological brain function. Direct invasive measurements of the electrical activity on the other hand produce too local a view which fails to show the large scale coherence which sustains awareness and cognition. On the opposite extreme, gross measures of the electrical activity like Electroencephalography (EEG) and single or few channel Magetoencephalography (MEG) had until recently to rely on simplistic point like models extracted from the averages of many repetitions of physiologically irrelevant stimuli. The introduction of multichannel probes with over 30 channels (Hämälainen et al. 1993), and the use of distributed source analysis (Ioannides et al. 1990a) opened up for the first time the possibility to study the response of single trials. In this work we address directly the question how representative is the description of events extracted from the analysis of the average signal. We use the simplest possible example: the cortical response to a simple 1 kHz tone, focusing on the early and by general admission “automatic” response around 100 ms after stimulus onset. To avoid the confounding inter-subject variability we have studied the responses over the left and right cortical areas to ipsi- and contralateral stimulation in a single subject; for testing reproducibility, we have used both the eyes open and eyes closed conditions. Since the computational demands involved in extracting a full three dimensional description from each trial are too great,we have complemented the distributed source analysis with special techniques, which allow us to scan through each and every single trial and identify each cortical activation similar to the ones picked out in the average signal. We are thus able to show conclusively that the sequence of events suggested by the analysis of the average signal is not representative of what is happening in individual trials. The sequence is made up of events which occurred in different trials reflecting probably the existence of many parallel routes each of which leads from the input at the ear to a final “computation”.

Key words

Human auditory evoked fields Magnetoencephalography (MEG) 40-Hz Oscillation Single epoch analysis Vector signal transformation V3 Spatiotemporal correlation measures 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Ahonen, A.I., Hämälainen, M.S., Kajola, M.J., Knuutila, J.E., Laine, P.L., Lounasmaa, O.V., and et al. A 122-channel Magnetometer Covering the Whole Head. In: A. Dittmar and J.C. Froment (Ed.), Proceedings of the Satellite Symposium on Neuroscience and Technology, 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering and Medicine and Biology Society, Lyon, France, Nov. 1992: 16–20.Google Scholar
  2. Basar, E., Basar-Eroglu, C., Röschke, J. and Schütt, A. The EEG is a Quasi-deterministic Signal Anticipating Sensory-Cognitive Tasks. In: E. Basar, and T.H. Bullock (Ed.), Brain Dynamics. Springer-Verlag Berlin Heidelberg, 1989: 43–71.Google Scholar
  3. Bamidis, P.D., Hellstrand, E., Lidholm, H., Abraham-Fuchs, K., and Ioannides, A.A. MFT in Complex Partial Epilepsy: Spatial-temporal Estimates of Interictal Activity. Neuroreport, 1995, 7(1): 17–23.PubMedGoogle Scholar
  4. Desimone, R., Albright, T.D., Gross, C.G. and Bruce, C. Stimulus-selective Properties of Inferior Temporal Neurons in the Macaque. J. Neurosc., 1984, 4: 2051–2062.Google Scholar
  5. Franowicz, M.N., and Barth, D.S. Comparison of Evoked Potentials and High-Frequency (Gamma-Band) Oscillating Potentials in Rat Auditory Cortex. Journal of Neurophysiology, 1995, 74: 96–111.PubMedGoogle Scholar
  6. Fuchs, M., Wagner, M., Wischmann, H.A., and Dössel, O. Cortical Current Imaging by Morphologically Constrained Reconstructions. In: C. Baumgartner, L. Deeke, G. Stroink and S.J. Williamson (Ed., Biomagnetism: Fundamental Research and Clinical Applications, Elsevier Science Publishers, Amsterdam, 1995: 320–325.Google Scholar
  7. Galambos, R., Myers, R.E., and Sheatz, G.C. Extralemniscal Activation of Auditory Cortex in Cats. The American Journal of Physiology, 1961, 200(1): 23–28.PubMedGoogle Scholar
  8. Galambos, R. EPIC X: Past, Present, Future. In: G. Karmos, M. Molnár, V. Csépe, I. Czigler and J.E. Desmedt (Ed.), Perspectives of Event-Related Potentials Research (EEG Suppl. 44). Elsevier Science Publishers, Amsterdam, 1995: 3–20.Google Scholar
  9. Grimes, D.I.F., Lennard, R.F. and Swithenby, S.J. Macroscopic Ionic Currents within the Human Leg. Phys. Med. Biol., 1985, 30(10): 1101–1112.Google Scholar
  10. Grimes, D.I.F. and Ioannides, A.A. Reconstructing 3-dimensional Line Current Sources from Magnetic Field Data.In: K. Atsumi, M. Kotani, S. Ueno, T. Katila, and S.J. Williamson (Ed.), Biomagnetism 87. Tokyo Denki University Press, 1988: 134–137.Google Scholar
  11. Grummich, P., Vieth, J., Kober, H., Pongratz, H., Ulbricht, D., and Ganslandt, O. Localization of Focal Spontaneous Beta Wave Activity Associated with Structural Lesions in the Brain. In: C. Baumgartner, L. Deeke, G. Stroink and S.J. Williamson (Ed.), Biomagnetism: Fundamental Research and Clinical Applications. Elsevier Science Publishers, Amsterdam, 1995: 75–79.Google Scholar
  12. Haig, A.R., Gordon, E., Rogers, G. and Anderson, J. Classification of Single-trial ERP Sub-types: Application of Globally Optimal Vector Quantization using Simulated Annealing. Electroenceph. Clin. Neurophysiol., 1995, 94: 288–297.PubMedGoogle Scholar
  13. Hämälainen, M.S., Hari, R., Iimoniemi, R.J., Knuutila, J. and Lounasmaa, O.V. Magnetoencephalography — Theory, Instrumentation, and Applications to Noninvasive Studies of the Working Human Brain. Reviews of Modern Physics, 1993, 65(2): 413–497.Google Scholar
  14. Hjorth, B. An On-line Transformation of EEG Scalp Potentials into Orthogonal Source Derivations. Electroenceph. Clin. Neurophysiol., 1975, 39: 526–530.PubMedGoogle Scholar
  15. Hosaka, H. and Cohen, D. Visual Determination of Generators of the Magnetocardiogram. J. Electrocardiology, 1976, 9: 426–432.Google Scholar
  16. Hubel, D.H., and Wiesel, T.N. Receptive Fields, Binocular Interaction and Functional Architectures of the Cat's Visual Cortex. J. Physiol., 1962, 160: 106–154.PubMedGoogle Scholar
  17. Ioannides, A.A. Graphical Solutions and Representations for the Biomagnetic Inverse Problem. In: P.C. Sabatier (Ed.), Advances in Electronics and Electron Physics Supplement 19, Inverse Problems: an Interdisciplinary Study. Academic press, Orlando, 1987: 205–216.Google Scholar
  18. Ioannides, A.A., Bolton, J.P.R. and Clarke, C.J.S. Continuous Probabilistic Solutions to the Biomagnetic Inverse Problem. Inverse Problem, 1990a, 6: 523–542.Google Scholar
  19. Ioannides, A.A., Hasson, R. and Miseldine, G.J. Model-dependent Noise Elimination and Distributed Source Solutions for the Biomagnetic Inverse Problem. In: A.F. Gmitro and etal. (Ed.), Digital Image Synthesis and Inverse Optics, 1990b, Proc. SPIE 1351: 471–481.Google Scholar
  20. Ioannides, A.A., Hellstrand, E., and Abraham-Fuchs, K. Point and Distributed Current Density Analysis of Interictal Epileptic Activity Recorded by Magnetoencephalography. Physiological Measurements, 1993a, 14: 121–130.Google Scholar
  21. Ioannides, A.A., Singh, K.D., Hasson, R., Baumann, S.B., Rogers, R.L., Guinto, F.C. and Papanicolaou, A.C. Comparison of Current Dipole and Magnetic Field Tomography Analyses of the Cortical Response to Auditory Stimuli. Brain Topography, 1993b, 6: 27–34.PubMedGoogle Scholar
  22. Ioannides, A.A. Searchlights into the Brain. The Open University (U.K.) video, August 1993.Google Scholar
  23. Ioannides, A.A. Estimates of Brain Activity using Magnetic Field Tomography and Large Scale Communication within the Brain. In: M.W. Ho, F.A. Popp, and U. Warnke (Ed.), Bioelectrodynamics and Biocommunication. World Scientific, Singapore, 1994a: 319–353.Google Scholar
  24. Ioannides, A.A. Estimates of 3D Brain Activity ms by ms from Biomagnetic Signals: Method (MFT), Results and their Significance. In: E. Eiselt, U. Zwiener, and H. Witte (Ed.), Quantitative and Topological EEG and MEG Analysis. Universitätsverlag Druckhaus-Maayer GmbH, Jena, 1994b: 59–68.Google Scholar
  25. Ioannides, A.A., Stephan, K.M., Fenwick, P.B.C., Lumsden, J., Fenton, G.W., Liu, M.J., Vieth, J., Squires, K.C., Lawson, D., Myers, R., Fink, G.R. and Frackowiak, R.S.J. Analysis of MEG Signals from a GO/NOGO Avoidance Paradigm and Comparison of Estimates of Brain Activity using PET. In: C. Baumgartner, L. Deeke, G. Stroink, and S.J. Williamson (Ed.), Biomagnetism: Fundamental Research and Clinical Applications. Elsevier Science Publishers, Amsterdam, 1995a: 262–265.Google Scholar
  26. Ioannides, A.A., Liu, M.J., Liu, L.C., Bamidis, P.D., Hellstrand, E. and Stephan, K.M. Magnetic Field Tomography of Cortical and Deep Processes: Examples of “Real-Time Mapping” of Averaged and Single Trial MEG Signals. International Journal of Psychophysiology, 1995b, 20(3): 161–175.PubMedGoogle Scholar
  27. Joliot, M., Ribary, U. and Llinás, R. Human Oscillatory Brain Activity near 40 Hz Coexists with Cognitive Temporal Binding. Proc. Natl. Acad. Sci. USA, 1994, 91: 11748–11751.PubMedGoogle Scholar
  28. Liberati, D., DiCorrado, S. and Mandelli, S. Topographic Mapping of Single Sweep Evoked Potentials in the Brain. IEEE Transactions on Biomedical Engineering, 1992, 39(9): 943–951.PubMedGoogle Scholar
  29. Liu, L.C. and Ioannides, A.A. Single Epoch Analysis of MEG Signals. In: C. Baumgartner, L. Deeke, G. Stroink, and S.J. Williamson (Ed.), Biomagnetism: Fundamental Research and Clinical Applications. Elsevier Science Publishers, Amsterdam, 1995: 439–444.Google Scholar
  30. Liu, L.C. Single Epoch Analysis and Bi-hemispheric Study of Magnetoencephalographic (MEG) Signals using Vector Signal Transformation V3 and Magnetic Field Tomography (MFT). PhD thesis, Department of Physics, The Open University, U.K., February 1995.Google Scholar
  31. Liu, M.J., Hasson, R. and Ioannides, A.A. A Transputer-based System for Magnetic Field Tomography. In R. Grebe, J. Hektor, S.C. Hilton, M. Jane, and P.H. Welch (Ed.), Transputer Applications and Systems'93. IOS Press, Amsterdam, 1993, 2: 1290–1297.Google Scholar
  32. Llinás, R. and Pare, D. Commentary of Dreaming and Wakefulness. Neuroscience, 1991, 44: 521–535.PubMedGoogle Scholar
  33. Llinás, R. and Ribary, U. Coherent 40-Hz Oscillation Characterizes Dream State in Humans. Proc. Natl. Acad. Sci. USA, 1993, 90: 2078–2081.PubMedGoogle Scholar
  34. Llinás, R., Ribary, U., Joliot, M., and Wang, X.J. Content and Context in Temporal Thalamo-cortical Binding. In: G. Buzsáki and et al. (Ed.), Temporal Coding in the Brain. Spring-Varlag Berlin Heidelberg, 1994: 251–272.Google Scholar
  35. Lütkenhöner, B., Pantev, C., Grunwald, A., and Menninghaus, E. Source-space Projection of Single Trials of the Auditory Evoked Field. In: C. Baumgartner, L. Deeke, G. Stroink, and S.J. Williamson (Ed.), Biomagnetism: Fundamental Research and Clinical Applications, Elsevier Science Publishers, Amsterdam, 1995: 347–351.Google Scholar
  36. Mitzdorf, U., Li, B.H. and Pöppel, E. Mass-action View of Single-cell Responses to Stimulation of the Receptive Field and/or beyond: Exemplification with Data from the Rabbit Primary Visual Cortex. Electroenceph. Clin. Neurophysiol, 1994, 92: 442–455.PubMedGoogle Scholar
  37. Nicolelis, M.A., Baccala, L.A., Lin, R.C., and Chapin, J.K. Sensorimotor Encoding by Synchronous Neural Ensemble Activity at Multiple Levels of the Somatosensory System. Science, 1995, 268: 1353–1358.PubMedGoogle Scholar
  38. O'Keefe, J. and Recce, M.L. Phase Relationship between Hippocampal Place Units and the EEG Theta Rhythm. Hippocampus, 1993, 3: 317–330.PubMedGoogle Scholar
  39. Pöppel, E. Temporal Mechanisms in Perception. International Review of Neurobiology, 1994, 37: 185–202.PubMedGoogle Scholar
  40. Ribary, U., Ioannides, A.A., Singh, K.D., Hasson, R., Bolton, J.P.R., Lado, F., Mogilner, A., and Llinás, R. Magnetic Field Tomography (MFT) of Coherent Thalamo-Cortical 40 Hz Oscillations in Humans. Proc. Natl. Acad. Sci. USA, 1991, 88: 11037–11041.PubMedGoogle Scholar
  41. Rieke, K., Gallen, C.C., Sobel, D.F., Otis, S., Bernstein, E., Schwartz, B., and Hirschkoff, E. Magnetic Source Imaging in Cerebrovascular Diseases. In: C. Baumgartner, L. Deeke, G. Stroink and S.J. Williamson (Ed.), Biomagnetism: Fundamental Research and Clinical Applications. Elsevier Science Publishers, Amsterdam, 1995: 46–49.Google Scholar
  42. Schwartz, B.J., Gallen, C.C., Aung, M., Sobel, D.F., Hirschkoff, E.G., and Bloom, F.E. Magnetoencephalographic Detection of Focal Slowing Associated with Head Trauma. In: C. Baumgartner, L. Deeke, G. Stroink, and S.J. Williamson (Ed.), Biomagnetism: Fundamental Research and Clinical Applications. Elsevier Science Publishers, Amsterdam, 1995: 66–69.Google Scholar
  43. Semmes, J. Hemispheric Specialization: A Possible Clue to Mechanism. Neurophysiologia, 1968, 6: 11–26.Google Scholar
  44. Simpson, G.V., Pflieger, M.E., Foxe, J.J., Ahlfors, S.P., Vaughan, H.G., Hrabe, J., Ilmoniemi, R.J. and Lantos, G. Dynamic Neuroimaging of Brain Function. J. Clin. Neurophysiol., 1995, 12(5): 432–449.PubMedGoogle Scholar
  45. Singh, K.D., Ioannides, A.A., Gray, N., Kober, H., Pongratz, H., Daun, A., Grummich, P. and Vieth, J. Distributed Current Analyses of Bi-hemispheric Magnetic Nlm Responses to Ipsi/Contralateral Monaural Stimulus from a Single Subject. Electroenceph. Clin. Neurophysiol., 1994, 92: 365–368.Google Scholar
  46. Snyder, A.Z., Abdullaev, Y.G., Posner, M.I. and Raichle, M.E. Scalp Electrical Potentials Reflect Regional Cerebral Blood Flow Responses during Processing of Written Words. Proc. Natl. Acad. Sci. USA, 1995, 92: 1689–1693.PubMedGoogle Scholar
  47. Tiitinen, H., Sinkkonen, J., Reinikainen, K., Alho, K., Lavikainen, J., and Näätänen, R. Selective Attention Enhances the Auditory 40-Hz Transient Response in Humans. Nature, 1993, 364: 59–60.PubMedGoogle Scholar
  48. Tomberg, C. and Desmedt, J.E. A Method for Identifying Short-Latency Human Cognitive Potentials in Single Trials by Scalp Mapping. Neuroscience Letters, 1995, 168: 123–125.Google Scholar
  49. Vaadia, E., Haalman, I., Abeles, M., Bergman, H., Prut, Y., Slovin, H., and Aertsen, A. Dynamics of Neuronal Interactions in Monkey Cortex in Relation to Behavioural Events. Nature, 1995, 373: 515–518.PubMedGoogle Scholar

Copyright information

© Human Sciences Press, Inc 1996

Authors and Affiliations

  • Lichan Liu
    • 1
  • Andreas A. Ioannides
    • 1
  1. 1.Department of PhysicsThe Open UniversityMilton KeynesUK

Personalised recommendations