Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2009, Article ID 652078, 7 pages
http://dx.doi.org/10.1155/2009/652078
Research Article

The Track of Brain Activity during the Observation of TV Commercials with the High-Resolution EEG Technology

1IRCCS, Fondazione Santa Lucia, 00179 Rome, Italy
2Dipartimento di Informatica e Sistemistica, Università di Roma “La Sapienza”, 00185 Rome, Italy
3Department of Physiology and Pharmacology, University of Rome “La Sapienza”, 00185 Rome, Italy
4Department of Neuroscience, University of Rome “Tor Vergata”, 00100 Rome, Italy

Received 15 December 2008; Accepted 8 April 2009

Academic Editor: Andrzej Cichocki

Copyright © 2009 Laura Astolfi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. T. Ambler, S. Braeutigam, J. Stins, S. P. R. Rose, and S. Swithenby, “Salience and choice: neural correlates of shopping decisions,” Psychology and Marking, vol. 21, no. 4, pp. 247–261, 2004. View at Publisher · View at Google Scholar
  2. S. Braeutigam, S. P. R. Rose, S. J. Swithenby, and T. Ambler, “The distributed neuronal systems supporting choice-making in real-life situations: differences between men and women when choosing groceries detected using magnetoencephalography,” European Journal of Neuroscience, vol. 20, no. 1, pp. 293–302, 2004. View at Publisher · View at Google Scholar
  3. J. Cappo, The Future of Advertising: New Media, New Clients, New Consumers in the Post-Television Age, McGraw-Hill, New York, NY, USA, 2005.
  4. H. E. Krugman, “Brain wave measures of media involvement,” Journal of Advertising Research, vol. 11, no. 9, pp. 3–9, 1971. View at Google Scholar
  5. A. A. Ioannides, L. Liu, D. Theofilou et al., “Real time processing of affective and cognitive stimuli in the human brain extracted from MEG signals,” Brain Topography, vol. 13, no. 1, pp. 11–19, 2000. View at Publisher · View at Google Scholar
  6. T. Ambler and T. Burne, “The impact of affect on memory of advertising,” Journal of Advertising Research, vol. 39, no. 2, pp. 25–34, 1999. View at Google Scholar
  7. M. Rotschild and J. Hyun, “Predicting memory for components of TV commercials from EEC,” Journal of Consumer Research, pp. 472–478, 1989. View at Google Scholar
  8. J. R. Rossiter and R. B. Silberstein, “Brain-imaging detection of visual scene encoding in long-term memory for TV commercials,” Journal of Advertising Research, vol. 41, no. 2, pp. 13–21, 2001. View at Google Scholar
  9. C. Young, “Brain waves, picture sorts®, and branding moments,” Journal of Advertising Research, vol. 42, no. 4, pp. 42–53, 2002. View at Google Scholar
  10. P. L. Nunez, Neocortical Dynamics and Human EEG Rhythms, Oxford University Press, New York, NY, USA, 1995.
  11. X. Bai, V. L. Towle, E. J. He, and B. He, “Evaluation of cortical current density imaging methods using intracranial electrocorticograms and functional MRI,” NeuroImage, vol. 35, no. 2, pp. 598–608, 2007. View at Publisher · View at Google Scholar
  12. B. He, Y. Wang, and D. Wu, “Estimating cortical potentials from scalp EEG's in a realistically shaped inhomogeneous head model by means of the boundary element method,” IEEE Transactions on Biomedical Engineering, vol. 46, no. 10, pp. 1264–1268, 1999. View at Publisher · View at Google Scholar
  13. A. M. Dale, A. K. Liu, B. R. Fischl et al., “Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity,” Neuron, vol. 26, no. 1, pp. 55–67, 2000. View at Publisher · View at Google Scholar
  14. F. Babiloni, F. Cincotti, C. Babiloni et al., “Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function,” NeuroImage, vol. 24, no. 1, pp. 118–131, 2005. View at Publisher · View at Google Scholar
  15. L. Astolfi, F. De Vico Fallani, S. Salinari et al., “Brain activity related to the memorization of TV commercials,” International Journal of Bioelectromegnetism, vol. 10, no. 3, pp. 1–10, 2008. View at Google Scholar
  16. J. Le and A. Gevins, “Method to reduce blur distortion from EEG's using a realistic head model,” IEEE Transactions on Biomedical Engineering, vol. 40, no. 6, pp. 517–528, 1993. View at Publisher · View at Google Scholar
  17. A. Gevins, J. Le, N. K. Martin, P. Brickett, J. Desmond, and B. Reutter, “High resolution EEG: 124-channel recording, spatial deblurring and MRI integration methods,” Electroencephalography and Clinical Neurophysiology, vol. 90, no. 5, pp. 337–358, 1994. View at Publisher · View at Google Scholar
  18. F. Babiloni, C. Babiloni, L. Locche, F. Cincotti, P. M. Rossini, and F. Carducci, “High-resolution electro-encephalogram: source estimates of Laplacian-transformed somatosensory-evoked potentials using a realistic subject head model constructed from magnetic resonance images,” Medical and Biological Engineering and Computing, vol. 38, no. 5, pp. 512–519, 2000. View at Publisher · View at Google Scholar
  19. F. Babiloni, C. Babiloni, F. Carducci et al., “High resolution EEG: a new model-dependent spatial deblurring method using a realistically-shaped MR-constructed subject's head model,” Electroencephalography and Clinical Neurophysiology, vol. 102, no. 2, pp. 69–80, 1997. View at Publisher · View at Google Scholar
  20. R. Grave de Peralta Menendez and S. L. Gonzalez Andino, “Distributed source models: standard solutions and new developments,” in Analysis of Neurophysiological Brain Functioning, C. Uhl, Ed., pp. 176–201, Springer, New York, NY, USA, 1999. View at Google Scholar
  21. L. Astolfi, F. Cincotti, D. Mattia et al., “Comparison of different cortical connectivity estimators for high-resolution EEG recordings,” Human Brain Mapping, vol. 28, no. 2, pp. 143–157, 2007. View at Publisher · View at Google Scholar
  22. W. Klimesch, M. Doppelmayr, and S. Hanslmayr, “Upper alpha ERD and absolute power: their meaning for memory performance,” Progress in Brain Research, vol. 159, pp. 151–165, 2006. View at Publisher · View at Google Scholar
  23. E. Tulving, S. Kapur, F. I. Craik, M. Moscovitch, and S. Houle, “Hemispheric encoding/retrieval asymmetry in episodic memory: positron emission tomography findings,” Proceedings of the National Academy of Sciences of the United States of America, vol. 91, no. 6, pp. 2016–2020, 1994. View at Google Scholar
  24. S. Braeutigam, “Neuroeconomics—from neural systems to economic behaviour,” Brain Research Bulletin, vol. 67, no. 5, pp. 355–360, 2005. View at Publisher · View at Google Scholar
  25. C. Babiloni, F. Babiloni, F. Carducci et al., “Mapping of early and late human somatosensory evoked brain potentials to phasic galvanic painful stimulation,” Human Brain Mapping, vol. 12, no. 3, pp. 168–179, 2001. View at Google Scholar
  26. L. Astolfi, F. Cincotti, C. Babiloni et al., “Assessing cortical functional connectivity by linear inverse estimation and directed transfer function: simulations and application to real data,” Clin Neurophysiol, vol. 116, no. 4, pp. 32–920, 2005. View at Google Scholar
  27. A. Urbano, F. Babiloni, C. Babiloni, A. Ambrosini, P. Onorati, and P. M. Rossini, “Human short-latency cortical responses to somatosensory stimulation,” A High Resolution Study, NeuroReport, vol. 8, no. 15, pp. 3239–3243, 1997. View at Google Scholar
  28. A. Urbano, C. Babiloni, F. Carducci, L. Fattorini, P. Onorati, and F. Babiloni, “Dynamic functional coupling of high resolution EEG potentials related to unilateral internally triggered one-digit movements,” Electroencephalography and Clinical Neurophysiol, vol. 106, no. 6, pp. 477–487, 1998. View at Google Scholar
  29. L. Astolfi, F. De Vico Fallani, F. Cincotti et al., “Imaging functional brain connectivity patterns from high-resolution EEG and FMRI via graph theory,” Psychophysology, vol. 44, no. 6, pp. 880–93, 2007. View at Google Scholar
  30. F. De Vico Fallani, L. Astolfi, F. Cincotti et al., “Extracting information from cortical connectivity patterns estimated from high resolution EEG recordings: A theoretical graph approach,” Brain Topography, vol. 19, no. 3, pp. 36–125, 2007. View at Google Scholar