The issue of multiple univariate comparisons in the context of neuroelectric brain mapping: An application in a neuromarketing experiment

G. Vecchiato, F. De Vico Fallani, L. Astolfi, J. Toppi, F. Cincotti, D. Mattia, S. Salinari, F. Babiloni

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents some considerations about the use of adequate statistical techniques in the framework of the neuroelectromagnetic brain mapping. With the use of advanced EEG/MEG recording setup involving hundred of sensors, the issue of the protection against the type I errors that could occur during the execution of hundred of univariate statistical tests, has gained interest. In the present experiment, we investigated the EEG signals from a mannequin acting as an experimental subject. Data have been collected while performing a neuromarketing experiment and analyzed with state of the art computational tools adopted in specialized literature. Results showed that electric data from the mannequin's head presents statistical significant differences in power spectra during the visualization of a commercial advertising when compared to the power spectra gathered during a documentary, when no adjustments were made on the alpha level of the multiple univariate tests performed. The use of the Bonferroni or Bonferroni-Holm adjustments returned correctly no differences between the signals gathered from the mannequin in the two experimental conditions. An partial sample of recently published literature on different neuroscience journals suggested that at least the 30% of the papers do not use statistical protection for the type I errors. While the occurrence of type I errors could be easily managed with appropriate statistical techniques, the use of such techniques is still not so largely adopted in the literature.

Original languageEnglish
Pages (from-to)283-289
Number of pages7
JournalJournal of Neuroscience Methods
Volume191
Issue number2
DOIs
Publication statusPublished - Aug 2010

Keywords

  • Bonferroni adjustment
  • False discovery rate
  • Inverse problem
  • Neuromarketing
  • Statistical mapping
  • Type I errors

ASJC Scopus subject areas

  • Neuroscience(all)

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