The use of modern brain imaging techniques could be useful to understand what brain areas are involved in the observation of video clips related to commercial advertising, as well as for the support of political campaigns, and also the areas of Public Service Announcements (PSAs). In this paper we describe the capability of tracking brain activity during the observation of commercials, political spots, and PSAs with advanced high-resolution EEG statistical techniques in time and frequency domains in a group of normal subjects. We analyzed the statistically significant cortical spectral power activity in different frequency bands during the observation of a commercial video clip related to the use of a beer in a group of 13 normal subjects. In addition, a TV speech of the Prime Minister of Italy was analyzed in two groups of swing and “supporter” voters. Results suggested that the cortical activity during the observation of commercial spots could vary consistently across the spot. This fact suggest the possibility to remove the parts of the spot that are not particularly attractive by using those cerebral indexes. The cortical activity during the observation of the political speech indicated a major cortical activity in the supporters group when compared to the swing voters. In this case, it is possible to conclude that the communication proposed has failed to raise attention or interest on swing voters. In conclusions, high-resolution EEG statistical techniques have been proved to able to generate useful insights about the particular fruition of TV messages, related to both commercial as well as political fields. 1. Introduction Every day we are exposed to several solicitations for purchasing products, voting or supporting particular politicians and even improving our life style. Such pressure has become usual, being mediated by all the current media available, video, audio, and even internet. How and to what extent these messages could be detected and recognized by our brain is still not well understood. In fact, the study of brain responses to commercial and political announcements has been measured mainly by the hemodynamic responses of the different brain areas, by using the functional Magnetic Resonance Imaging devices (fMRI). However, both the stimuli and the relative brain responses have rapidly shifting characteristics that are not tracked by the evolution of the hemodynamic blood flow, which usually lasts 4–6 seconds. Different brain imaging tools, mainly EEG and Magnetoencephalography, exhibit a sufficient time resolution to follow the brain
References
[1]
P. L. Nunez, Electric Fields of the Brain: The Neurophysics of EEG, Oxford University Press, New York, NY, USA, 1981.
[2]
P. L. Nunez, Neocortical Dynamics and Human EEG Rhythms, Oxford University Press, New York, NY, USA, 1995.
[3]
F. De Vico Fallani, L. Astolfi, F. Cincotti, et al., “Cortical network topology during successful memory encoding in a lifelike experiment,” in Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '08), pp. 4007–4010, 2008.
[4]
T. Ambler, S. Braeutigam, J. Stins, S. P. R. Rose, and S. Swithenby, “Salience and choice: neural correlates of shopping decisions,” Psychology and Marketing, vol. 21, no. 4, pp. 247–261, 2004.
[5]
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.
[6]
J. Cappo, The Future of Advertising: New Media, New Clients, New Consumers in the Post-Television Age, McGraw-Hill, New York, NY, USA, 2005.
[7]
H. E. Krugman, “Brain wave measures of media involvement,” Journal of Advertising Research, vol. 11, pp. 3–10, 1971.
[8]
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.
[9]
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.
[10]
M. Rotschild and J. Hyun, “Predicting memory for components of TV commercials from EEC,” Journal of Consumer Research, pp. 72–78, 1989.
[11]
J. R. Rossiter, R. B. Silberstein, P. G. Harris, and G. A. Nield, “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.
[12]
C. Young, “Brain waves, picture sorts , and branding moments,” Journal of Advertising Research, vol. 42, no. 4, pp. 42–53, 2002.
[13]
Sands Research, http://sandsresearch.com/.
[14]
Emory University, Centre For Neuropolicy, http://www.neuropolicy.emory.edu/.
[15]
L. Biener, J. E. Harris, and W. Hamilton, “Impact of the Massachusetts tobacco control programme: Population based trend analysis,” British Medical Journal, vol. 321, pp. 351–354, 2000.
[16]
S. Emery, M. Wakefield, Y. Terry-McElrath, G. Szczypka, P. M. O'Malley, L. D. Johnston, et al., “Televised state-sponsored anti-tobacco advertising and youth smoking beliefs and behavior in the United States, 1999–2000,” Archives Pediatric Adolescent Medicine, vol. 159, pp. 639–645, 2005.
[17]
M. Wakefield, B. Flay, M. Nichter, and G. Giovino, “Effects of anti-smoking advertising on youth smoking: a review,” Journal of Health Communication, vol. 8, pp. 229–247, 2003.
[18]
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.
[19]
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.
[20]
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.
[21]
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.
[22]
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.
[23]
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.
[24]
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.
[25]
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, 2006.
[26]
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.
[27]
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.
[28]
A. Todorov, A. N. Mandisodza, A. Goren, and C. C. Hall, “Inferences of competence from faces predict election outcomes,” Science, vol. 308, pp. 1623–1626, 2005.
[29]
C. C. Ballew II and A. Todorov, “Predicting political elections from rapid and unreflective face judgments,” Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 46, pp. 17948–17953, 2007.
[30]
M. Iacoboni, J. Freedman, J. Kaplan, et al., “This is your brain on politics,” The New York Times, November 2007, http://www.nytimes.com/2007/11/11/opinion/11freedman.html?pagewanted=1&_r=2&sq=marco%20iacoboni&st=cse&scp=8.
[31]
F. De Vico Fallani, L. Astolfi, F. Cincotti, et al., “Cortical functional connectivity networks in normal and spinal cord injured patients: evaluation by graph analysis,” Human Brain Mapping, vol. 28, no. 12, pp. 1334–1346, 2007.
[32]
L. Astolfi, F. De Vico Fallani, F. Cincotti, et al., “Imaging functional brain connectivity patterns from high-resolution EEG and fMRI via graph theory,” Psychophysiology, vol. 44, no. 6, pp. 880–893, 2007.
[33]
L. Astolfi, F. Cincotti, D. Mattia, et al., “Tracking the time-varying cortical connectivity patterns by adaptive multivariate estimators,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 3, pp. 902–913, 2008.
[34]
M. Oliveri, C. Babiloni, M. M. Filippi, et al., “Influence of the supplementary motor area on primary motor cortex excitability during movements triggered by neutral or emotionally unpleasant visual cues,” Experimental Brain Research, vol. 149, no. 2, pp. 214–221, 2003.
[35]
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.
[36]
A. Urbano, C. Babiloni, P. Onorati, and F. Babiloni, “Dynamic functional coupling of high resolution EEG potentials related to unilateral internally triggered one-digit movements,” Electroencephalography and Clinical Neurophysiology, vol. 106, no. 6, pp. 477–487, 1998.