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. 2014 Sep 10:8:279.
doi: 10.3389/fnins.2014.00279. eCollection 2014.

Studentized continuous wavelet transform (t-CWT) in the analysis of individual ERPs: real and simulated EEG data

Ruben G L Real  1 , Boris Kotchoubey  2 , Andrea Kübler  1
Affiliations

Studentized continuous wavelet transform (t-CWT) in the analysis of individual ERPs: real and simulated EEG data

Ruben G L Real et al. Front Neurosci. .

Abstract

This study aimed at evaluating the performance of the Studentized Continuous Wavelet Transform (t-CWT) as a method for the extraction and assessment of event-related brain potentials (ERP) in data from a single subject. Sensitivity, specificity, positive (PPV) and negative predictive values (NPV) of the t-CWT were assessed and compared to a variety of competing procedures using simulated EEG data at six low signal-to-noise ratios. Results show that the t-CWT combines high sensitivity and specificity with favorable PPV and NPV. Applying the t-CWT to authentic EEG data obtained from 14 healthy participants confirmed its high sensitivity. The t-CWT may thus be well suited for the assessment of weak ERPs in single-subject settings.

Keywords: EEG; ERP; detection; electroencephalogram; significance; t-CWT; wavelet.

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Figures

Figure 1
Figure 1
A Mexican Hat Wavelet with a scale of 200 and a time shift of 400 ms.
Figure 2
Figure 2
Average of simulated EEG data at −16 dB.
Figure 3
Figure 3
Scalogram of Studentized wavelet coefficients corresponding to Figure 2. Highlighted area indicates location of significant (p < 0.05) differences. Plus sign indicates local maximum.
Figure 4
Figure 4
Means and 95% confidence intervals of the distribution of F1 after Monte Carlo simulation (5).
Figure 5
Figure 5
Grand average of activation following odd and frequent tone trials (N = 14).
See this image and copyright information in PMC

References

    1. Abramovich F., Benjamini Y. (1995). Thresholding of wavelet coefficients as multiple hypotheses testing procedure, in Lecture Notes in Statistics, eds Bickel P., Diggle P., Fienberg S., Krickeberg K., Olkin I., Wermuth N. (New York, NY: Springer New York; ), 5–14
    1. Acunzo D. J., MacKenzie G., van Rossum M. C. (2012). Systematic biases in early erp and erf components as a result of high-pass filtering. J. Neurosci. Methods 209, 212–218 10.1016/j.jneumeth.2012.06.011 - DOI - PubMed
    1. Benjamini Y., Hochberg Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300
    1. Benjamini Y., Krieger A. M., Yekutieli D. (2006). Adaptive linear step-up procedures that control the false discovery rate. Biometrika 93, 491–507 10.1093/biomet/93.3.491 - DOI - PubMed
    1. Benjamini Y., Yekutieli D. (2001). The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 29, 1165–1188 10.2307/2674075 - DOI - PubMed

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