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Nonparametric Statistical Analysis of Map Topographies on the Epoch Level

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Magnetoencephalography

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Abstract

Establishing the significance of observed effects is a requirement for meaningful interpretation of clinical or experimental data. Averaging is commonly used to increase the signal-to-noise-ratio (SNR) of brain responses recorded in an event-related potential (ERP) or event-related field (ERF) experiment. However, the individual epochs collected contain additional information beyond what is represented by averages. Specifically, consistency between brain responses to a certain stimulus type, as well as differences between stimulus types, can be established by statistically analyzing all of the individual epochs in an ERP or ERF data set. Topographic analysis of variance (TANOVA) and statistical nonparametric mapping (SnPM) are nonparametric permutation or randomization tests which have previously been published but mainly been used to process per-subject averaged EEG data in the context of group studies. This chapter describes how to apply TANOVA to individual epochs on a sample-by-sample basis, even in the context of single-subject data. TANOVA is able to identify latencies of significantly different map topographies.

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References

  • Koenig T, Melie-Garcia L (2009) Statistical analysis of multichannel scalp field data. In: Michel CM, Koenig T, Brandeis D, Gianotti LRR, Wackermann J (eds) Electrical neuroimaging. Cambridge University Press, Cambridge, pp 169–189

    Chapter  Google Scholar 

  • Lee C-Y, Yen H-L, Yeh P-W, Lin W-H, Cheng Y-Y, Tzeng Y-L, Wue H-C (2012) Mismatch responses to lexical tone, initial consonant, and vowel in Mandarin-speaking preschoolers. Neuropsychologia 50:3228–3239

    Article  Google Scholar 

  • Murray MM, Michel CM, Grave de Peralta R, Ortique S, Brunet D, Gonzalez Andino S et al (2004) Rapid discrimination of visual and multisensory memories revealed by electrical neuroimaging. NeuroImage 21:125–135

    Article  Google Scholar 

  • Murray MM, Brunet D, Michel CM (2008) Topographic ERP analyses: a step-by-step tutorial review. Brain Topogr 20:249–264

    Article  Google Scholar 

  • Wagner M, Ponton C, Tech R, Fuchs M, Kastner J (2014) Non-parametric statistical analysis of EEG/MEG map topographies and source distributions on the epoch level. Hum Cogn Neurophysiol 7:1–23

    Google Scholar 

  • Wagner M, Tech R, Fuchs M, Kastner J, Gasca F (2017) Statistical non-parametric mapping in sensor space. Biomed Eng Lett 7:193–203

    Article  Google Scholar 

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Authors and Affiliations

  1. Neuroscan, Compumedics Europe GmbH, Hamburg, Germany

    Michael Wagner

Authors
  1. Michael Wagner
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Correspondence to Michael Wagner .

Editor information

Editors and Affiliations

  1. Faculty of Science, Department of Physics, University of Zagreb, Zagreb, Croatia

    Selma Supek

  2. School of Medicine Department of Radiology, University of New Mexico, Albuquerque, NM, USA

    Cheryl J. Aine

Section Editor information

  1. Faculty of Science, Department of Physics, University of Zagreb, Zagreb, Croatia

    Selma Supek

  2. The Mind Research Network ,, Albuquerque, New Mexico, USA

    Cheryl J. Aine

  3. School of Medicine Department of Radiology, University of New Mexico, Albuquerque, USA

    Cheryl J. Aine

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Wagner, M. (2019). Nonparametric Statistical Analysis of Map Topographies on the Epoch Level. In: Supek, S., Aine, C. (eds) Magnetoencephalography. Springer, Cham. https://doi.org/10.1007/978-3-319-62657-4_13-1

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  • DOI: https://doi.org/10.1007/978-3-319-62657-4_13-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62657-4

  • Online ISBN: 978-3-319-62657-4

  • eBook Packages: Springer Reference EngineeringReference Module Computer Science and Engineering

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