Single-Trial Analysis of Bioelectromagnetic Signals: The Quest for Hidden Information

  • Maureen Clerc
  • Théodore Papadopoulo
  • Christian Bénar


This chapter deals with the analysis of multitrial electrophysiology datasets coming from neuroelectromagnetic recordings by electro-encephalography and magneto-encephalography (EEG and MEG). For such measurements, multitrial recordings are necessary in order to extract meaningful information. The obtained datasets present several characteristics: no ground-truth data, high level of noise (defined as the part of the data which is uncorrelated across trials), inter-trial variability. This chapter presents tools that deal with such datasets and their properties. The focus is on two families of data processing methods: data-driven methods, in a section on non-linear dimensionality reduction, and model-driven methods, in a section on Matching Pursuit and its extensions. The importance of correctly capturing the inter-trial variability is underlined in the last section which presents four case-studies in clinical and cognitive neuroscience.


Local Field Potential Brain Computer Interface Match Pursuit Pursuit Algorithm Raster Plot 
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.



The authors wish to thank Franck Vidal and Boris Burle for useful discussions. This article relates some work published with Alexandre Gramfort, Renaud Keriven and Bruno Torrésani. This work is partially funded by the French ANR project MultiModel.


  1. 1.
    S. Baillet, J.C. Mosher, and R.M. Leahy. Electromagnetic brain mapping. IEEE Signal Processing Magazine, 18(6):14–30, 2001.Google Scholar
  2. 2.
    M. Belkin and P. Niyogi. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Computation, 15:1373–1396, 2003.Google Scholar
  3. 3.
    C.G. Bénar, T. Papadopoulo, B. Torrésani, and M. Clerc. Consensus matching pursuit for multi-trial eeg signals. Journal of Neuroscience Methods, 180:161–170, 2009.Google Scholar
  4. 4.
    C.G. Bénar, D. Schön, S. Grimault, B. Nazarian, B. Burle, M. Roth, J.M. Badier, P. Marquis, C. Liegeois-Chauvel, and J.L. Anton. Single-trial analysis of oddball event-related potentials in simultaneous EEG-fMRI. Human Brain Mapping, 28:602–613, 2007.Google Scholar
  5. 5.
    B. Burle, C. Roger, S. Allain, F. Vidal, and T. Hasbroucq. Error negativity does not reflect conflict: a reappraisal of conflict monitoring and anterior cingulate cortex activity. J. of Cogn. Neurosci., 20(9):1637–55, 2008.Google Scholar
  6. 6.
    P. Comon. Independent component analysis - a new concept? Signal Processing, 36, 1994.Google Scholar
  7. 7.
    J. de Munck, F. Bijma, P. Gaura, C. Sieluzycki, M. Branco, and R. Heethaar. A maximum-likelihood estimator for trial-to-trial variations in noisy MEG/EEG data sets. IEEE Trans. Biomed. Eng., 51(12):2123–28, 2004.Google Scholar
  8. 8.
    S. Debener, M. Ullsperger, M. Siegel, K. Fiehler, D. von Cramon, and A. Engel. Trial-by-trial coupling of concurrent electroencephalogram and functional magnetic resonance imaging identifies the dynamics of performance monitoring. Neuroscience, 2005.Google Scholar
  9. 9.
    T. Eichele, K. Specht, M. Moosmann, M. Jongsma, R. Quian Quiroga, H. Nordby, and K. Hugdahl. Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI. Proc Natl Acad Sci U.S.A., 2005.Google Scholar
  10. 10.
    A. Gramfort, R. Keriven, and M. Clerc. Graph-based variability estimation in single-trial event-related neural responses. IEEE Trans. Biomed. Engin., 56(5):1051–1061, 2010.Google Scholar
  11. 11.
    R. Gribonval, H. Rauhut, K. Schnass, and P. Vandergheynst. Atoms of all channels, unite! Average case analysis of multi-channel sparse recovery using greedy algorithms. The Journal of Fourier Analysis and Applications, 14(5):655–687, 2008.Google Scholar
  12. 12.
    M. Hein, J. Audibert, and U. von Luxburg. Graph Laplacians and their convergence on random neighborhood graphs. The Journal of Machine Learning Research, 8:1325–1370, 2007.Google Scholar
  13. 13.
    A. Holm, P. Ranta-aho, M. Sallinen, P. Karjalainen, and K. Müller. Relationship of P300 single-trial responses with reaction time and preceding stimulus sequence. Int. J. Psychophysiol., 2006.Google Scholar
  14. 14.
    T.P. Jung, S. Makeig, M. Westerfield, J. Townsend, E. Courchesne, and T.J. Sejnowski. Analysis and visualization of single-trial event-related potentials. Human Brain Mapping, 14:166–185, 2001.Google Scholar
  15. 15.
    M. Kutas, G. McCarthy, and E. Donchin. Augmenting mental chronometry: the P300 as a measure of stimulus evaluation time. Science, 197:792–795, August 1977.Google Scholar
  16. 16.
    D. Lehmann and W. Skrandies. Spatial analysis of evoked potentials in man - a review. Progr Neurobiol, 23(3):227–250, 1984.Google Scholar
  17. 17.
    S. Mallat and Z. Zhang. Matching pursuit with time-frequency dictionaries. IEEE Trans. on Signal Processing, 41(12):3397–3414, 1993.Google Scholar
  18. 18.
    C. Mulert, V. Kirsch, R. Pascual-Marqui, R.W. McCarley, and K.M. Spencer. Long-range synchrony of gamma oscillations and auditory hallucination symptoms in schizophrenia. International Journal of Psychophysiology, 79(1):55–63, January 2011. Special Issue: Correlations between gamma-band oscillations and human behaviour.Google Scholar
  19. 19.
    J. Polich. Clinical application of the p300 event-related brain potential. Physical Medicine & Rehabilitation Clinics of North America, 15(133), 2004.Google Scholar
  20. 20.
    R. Quian Quiroga and E. van Luijtelaar. Habituation and sensitization in rat auditory evoked potentials: A single-trial analysis with wavelet denoising. International Journal of Psychophysiology, 43(2):141–153, 2002.Google Scholar
  21. 21.
    C. Tallon-Baudry, O. Bertrand, C. Delpuech, and J. Pernier. Stimulus specificity of phase-locked and non-phase-locked 40 Hz visual responses in human. J. Neurosci., 16(13):4240–4249, 1996.Google Scholar
  22. 22.
    F. Vidal, B. Burle, M. Bonnet, J. Grapperon, and T. Hasbroucq. Error negativity on correct trials: a reexamination of available data. Biol. Psychol., 64(3):265–82, 2003.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Maureen Clerc
    • 1
  • Théodore Papadopoulo
    • 1
  • Christian Bénar
    • 2
  1. 1.Inria Sophia Antipolis MéditerranéeAthena project-teamSophia AntipolisFrance
  2. 2.Institut des Neurosciences des Systèmes -INS, UMR 1106 INSERMAix-Marseille Université, Faculté de Médecine La TimoneMarseille Cedex 05France

Personalised recommendations