Advertisement

pp 1-28 | Cite as

Multivariate Methods to Track the Spatiotemporal Profile of Feature-Based Attentional Selection Using EEG

  • Johannes Jacobus Fahrenfort
Protocol
Part of the Neuromethods book series

Abstract

This chapter provides a tutorial style guide to analyzing electroencephalogram (EEG) data contingent on feature-based attentional selection. It is targeted at researchers that currently investigate attentional processes using univariate methods but consider moving to multivariate analyses. The chapter starts by providing examples of classical univariate analysis, in which the EEG signal occurring ipsilateral to the target is subtracted from the signal that occurs in a contralateral electrode (i.e., the classical N2pc, an interhemispheric posterior negativity emerging around 180–200 ms). Next, it shows how the same type of information can also be identified using multivariate pattern analysis (MVPA). MVPA does not restrict one to contrast attentional selection in opposite hemifields but also allows one to assess attentional selection on the vertical meridian, or even within a quadrant of the visual field, opening up new avenues for research. The chapter demonstrates how to visualize topographic maps of attentional selection when using MVPA and shows how to assess timing onsets using the percent-amplitude latency method. Finally, it shows how a forward encoding model enables one to characterize the relationship between a continuous experimental variable (such as attended targets positioned on a circle) and EEG activity. This allows one to construct brain patterns for positions in the visual field that were never attended in the data that was used to create the forward model. This chapter is intended as a practical guide, explaining the methods and providing the scripts that can be used to generate the figures in-line, thus providing a step-by-step cookbook for analyzing neural time series data in the field of feature-based attentional selection.

Keywords

Feature-based attention Attentional selection EEG Univariate analysis N2pc MVPA Multivariate pattern analysis Classification Decoding BDM Forward encoding model Inverted encoding model FEM 

Notes

Acknowledgments

I would like to thank Anna Grubert, Martin Eimer, and Chris Olivers for allowing me to freely use and share the data from these experiments, as well as the analysis plans that we used on these data. This chapter would not have existed without them.

References

  1. 1.
    Eimer M (1996) The N2pc component as an indicator of attentional selectivity. Electroencephalogr Clin Neurophysiol 99(3):225–234.  https://doi.org/10.1016/0013-4694(96)95711-9CrossRefGoogle Scholar
  2. 2.
    Luck SJ, Hillyard SA (1994) Electrophysiological correlates of feature analysis during visual search. Psychophysiology 31(3):291–308Google Scholar
  3. 3.
    Woodman GF (2010) A brief introduction to the use of event-related potentials (ERPs) in studies of perception and attention. Atten Percept Psychophys 72(8):2031–2046.  https://doi.org/10.3758/APP.72.8.2031Google Scholar
  4. 4.
    Fahrenfort JJ, Grubert A, Olivers CNL, Eimer M (2017) Multivariate EEG analyses support high-resolution tracking of feature-based attentional selection. Sci Rep 7(1):1886.  https://doi.org/10.1038/s41598-017-01911-0CrossRefGoogle Scholar
  5. 5.
    Fahrenfort JJ, van Driel J, van Gaal S, Olivers CNL (2018) From ERPs to MVPA using the Amsterdam decoding and modeling toolbox (ADAM). Front Neurosci 12.  https://doi.org/10.3389/fnins.2018.00368
  6. 6.
    Grootswagers T, Wardle SG, Carlson TA (2017) Decoding dynamic brain patterns from evoked responses: a tutorial on multivariate pattern analysis applied to time series neuroimaging data. J Cogn Neurosci 29(4):677–697.  https://doi.org/10.1162/jocn_a_01068CrossRefGoogle Scholar
  7. 7.
    van Driel J, Olivers CNL, Fahrenfort JJ (2019) High-pass filtering artifacts in multivariate classification of neural time series data. bioRxiv.  https://doi.org/10.1101/530220
  8. 8.
    VanRullen R (2011) Four common conceptual fallacies in mapping the time course of recognition. Front Psychol 2:365.  https://doi.org/10.3389/fpsyg.2011.00365Google Scholar
  9. 9.
    Maris E, Oostenveld R (2007) Nonparametric statistical testing of EEG- and MEG-data. J Neurosci Methods 164(1):177–190.  https://doi.org/10.1016/J.Jneumeth.2007.03.024Google Scholar
  10. 10.
    Sassenhagen J, Draschkow D (2019) Cluster-based permutation tests of MEG/EEG data do not establish significance of effect latency or location. Psychophysiology 35(2):e13335.  https://doi.org/10.1111/psyp.13335Google Scholar
  11. 11.
    Kiesel A, Miller J, Jolicoeur P, Brisson B (2008) Measurement of ERP latency differences: a comparison of single-participant and jackknife-based scoring methods. Psychophysiology 45(2):250–274.  https://doi.org/10.1111/j.1469-8986.2007.00618.xGoogle Scholar
  12. 12.
    Luck SJ (2014) An introduction to the event-related potential technique. MIT Press, Cambridge, MA.  https://doi.org/10.1086/506120CrossRefGoogle Scholar
  13. 13.
    Liesefeld HR (2018) Estimating the timing of cognitive operations with MEG/EEG latency measures: a primer, a brief tutorial, and an implementation of various methods. Front Neurosci 12:765.  https://doi.org/10.3389/fnins.2018.00765CrossRefGoogle Scholar
  14. 14.
    King JR, Dehaene S (2014) Characterizing the dynamics of mental representations: the temporal generalization method. Trends Cogn Sci 18(4):203–210.  https://doi.org/10.1016/j.tics.2014.01.002Google Scholar
  15. 15.
    Allefeld C, Görgen K, Haynes J-D (2016) Valid population inference for information-based imaging: from the second-level t-test to prevalence inference. Neuroimage 141:378–392.  https://doi.org/10.1016/j.neuroimage.2016.07.040Google Scholar
  16. 16.
    Hand DJ, Till RJ (2001) A simple generalisation of the area under the ROC curve for multiple class classification problems. Mach Learn 45(2):171–186.  https://doi.org/10.1023/A:1010920819831Google Scholar
  17. 17.
    Miller J, Patterson T, Ulrich R (1998) Jackknife-based method for measuring LRP onset latency differences. Psychophysiology 35(1):99–115Google Scholar
  18. 18.
    Ulrich R, Miller J (2001) Using the jackknife-based scoring method for measuring LRP onset effects in factorial designs. Psychophysiology 38(5):816–827.  https://doi.org/10.1111/1469-8986.3850816Google Scholar
  19. 19.
    Haufe S, Meinecke F, Goergen K, Daehne S, Haynes J-D, Blankertz B, Biessgmann F (2014) On the interpretation of weight vectors of linear models in multivariate neuroimaging. Neuroimage 87:96–110.  https://doi.org/10.1016/j.neuroimage.2013.10.067CrossRefGoogle Scholar
  20. 20.
    Brouwer GJ, Heeger DJ (2009) Decoding and reconstructing color from responses in human visual cortex. J Neurosci 29(44):13992–14003.  https://doi.org/10.1523/JNEUROSCI.3577-09.2009Google Scholar
  21. 21.
    Garcia JO, Srinivasan R, Serences JT (2013) Near-real-time feature-selective modulations in human cortex. Curr Biol 23(6):515–522.  https://doi.org/10.1016/j.cub.2013.02.013Google Scholar
  22. 22.
    Ester EF, Sprague TC, Serences JT (2015) Parietal and frontal cortex encode stimulus-specific mnemonic representations during visual working memory. Neuron 87(4):893–905.  https://doi.org/10.1016/j.neuron.2015.07.013CrossRefGoogle Scholar
  23. 23.
    Foster JJ, Sutterer DW, Serences JT, Vogel EK, Awh E (2017) Alpha-band oscillations enable spatially and temporally resolved tracking of covert spatial attention. Psychol Sci 28(7):929–941.  https://doi.org/10.1177/0956797617699167CrossRefGoogle Scholar
  24. 24.
    Foster JJ, Sutterer DW, Serences JT, Vogel EK, Awh E (2016) The topography of alpha-band activity tracks the content of spatial working memory. J Neurophysiol 115(1):168–177.  https://doi.org/10.1152/jn.00860.2015CrossRefGoogle Scholar
  25. 25.
    Samaha J, Sprague TC, Postle BR (2016) Decoding and reconstructing the focus of spatial attention from the topography of alpha-band oscillations. J Cogn Neurosci 28(8):1090–1097.  https://doi.org/10.1162/jocn_a_00955Google Scholar
  26. 26.
    Gardner JL, Liu T (2019) Inverted encoding models reconstruct an arbitrary model response, not the stimulus. eNeuro 6(2). pii: ENEURO.0363-18.2019.  https://doi.org/10.1523/ENEURO.0363-18.2019Google Scholar
  27. 27.
    Liu T, Cable D, Gardner JL (2018) Inverted encoding models of human population response conflate noise and neural tuning width. J Neurosci 38(2):398–408.  https://doi.org/10.1523/JNEUROSCI.2453-17.2017CrossRefGoogle Scholar
  28. 28.
    Sprague TC, Adam KCS, Foster JJ, Rahmati M, Sutterer DW, Vo VA (2018) Inverted encoding models assay population-level stimulus representations, not single-unit neural tuning. eNeuro 5(3). pii: ENEURO.0098-18.2018.  https://doi.org/10.1523/eneuro.0098-18.2018Google Scholar
  29. 29.
    Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol 160:106–154Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2019

Authors and Affiliations

  • Johannes Jacobus Fahrenfort
    • 1
    • 2
    • 3
  1. 1.Department of PsychologyUniversity of AmsterdamAmsterdamThe Netherlands
  2. 2.Amsterdam Brain and Cognition (ABC)University of AmsterdamAmsterdamThe Netherlands
  3. 3.Department of Experimental and Applied PsychologyVrije Universiteit AmsterdamAmsterdamThe Netherlands

Personalised recommendations