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Development of a Computer Simulator of the Visual N2 Event-Related Potential Component for the Study of Cognitive Processes

  • Francesca Marturano
  • Sabrina Brigadoi
  • Mattia Doro
  • Roberto Dell’Acqua
  • Giovanni SparacinoEmail author
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 76)

Abstract

The importance of visual N2 Event-Related Potential (ERP) component in the study of cognitive processes lies in its interpretation as a measure of the allocation of visual attention to possible targets. Unfortunately, the N2 component has a small amplitude and the domain of validity of methods used for its estimation is difficult to assess if real data are considered. Here, we develop a computer simulator of ERP measurements emulating the variability of the visual N2 component and use the synthetic data to evaluate the performance of four popular literature ERP estimation methods. Results confirmed that a solid simulation framework could allow identifying a reliable method to detect small-amplitude ERP components and quantifying its accuracy.

Keywords

Visual N2 ERP modeling EEG signal processing Gaussian mixture models 

Notes

Conflict of Interest

None

References

  1. 1.
    Luck, S.J., Hillyard, S.A.: Electrophysiological correlates of feature analysis during visual search. Psychophysiology 31(3), 291–308 (1994)CrossRefGoogle Scholar
  2. 2.
    Eimer, M.: The N2pc component as an indicator of attentional selectivity. Electroencephalogr. Clin. Neurophysiol. 99(3), 225–234 (1996)CrossRefGoogle Scholar
  3. 3.
    Hickey, C., Di Lollo, V., McDonald, J.J.: Electrophysiological indices of target and distractor processing in visual search. J. Cogn. Neurosci. 21(4), 760–775 (2009)CrossRefGoogle Scholar
  4. 4.
    D’Avanzo, C., Goljahani, A., Pillonetto, G., De Nicolao, G., Sparacino, G.: A multi-task learning approach for the extraction of single-trial evoked potentials. Comput. Methods Programs Biomed. 110(2), 125–136 (2013)CrossRefGoogle Scholar
  5. 5.
    D’Avanzo, C., Schiff, S., Amodio, P., Sparacino, G.: A Bayesian method to estimate single-trial event-related potentials with application to the study of the P300 variability. J. Neurosci. Methods 198(1), 114–124 (2011)CrossRefGoogle Scholar
  6. 6.
    Ouyang, G., Hildebrandt, A., Sommer, W., Zhou, C.: Exploiting the intra-subject latency variability from single-trial event-related potentials in the P3 time range: a review and comparative evaluation of methods. Neurosci. Biobehav. Rev. 75, 1–21 (2017)CrossRefGoogle Scholar
  7. 7.
    Kiesel, A., Miller, J., Jolicœur, P., Brisson, B.: Measurement of ERP latency differences: a comparison of single-participant and jackknife-based scoring methods. Psychophysiology 45(2), 250–274 (2008)CrossRefGoogle Scholar
  8. 8.
    Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134(1), 9–21 (2004)CrossRefGoogle Scholar
  9. 9.
    Akaike, H.: A new look at the statistical model identification. IEEE Trans. Automat. Contr. 19(6), 716–723 (1974)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Steinberg, H.-W., Gasser, T., Franke, J.: Fitting autoregressive models to EEG time series: an empirical comparison of estimates of the order. IEEE Trans. Acoust. 33(1), 143–150 (1985)CrossRefGoogle Scholar
  11. 11.
    Cerutti, S., Chiarenza, G., Liberati, D., Mascellani, P., Paves, G.: A parametric method of identification. IEEE Trans. Biomed. Eng. 35(9), 701–711 (1988)CrossRefGoogle Scholar
  12. 12.
    Anderson, C.W., Stolz, E.A., Shamsunder, S.: Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks. IEEE Trans. Biomed. Eng. 45(3), 277–286 (1998)CrossRefGoogle Scholar
  13. 13.
    Riedel, H., Granzow, M., Kollmeier, B.: Single-sweep-based methods to improve the quality of auditory brain stem responses part II: averaging methods. Zeitschrift für Audiol. 40(2), 62–85 (2001)Google Scholar
  14. 14.
    Fukami, T., Watanabe, J., Ishikawa, F.: Robust estimation of event-related potentials via particle filter. Comput. Methods Programs Biomed. 125, 26–36 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of PadovaPaduaItaly

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