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References and Further Reading

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Eric Fields edited this page Jun 20, 2022 · 18 revisions

Contents

Background for EEG/ERP data collection, processing, analysis, and interpretation

  • Cohen, M. X. (2014). Analyzing Neural Time Series Data. Cambridge, MA: MIT Press.
  • Luck, S. J. (2014). An Introduction to the Event-Related Potential Technique (2nd ed.). Cambridge, MA: The MIT Press.

Introduction and overview of mass univariate statistics and corrections for ERP data

Power, multiple comparisons, researcher degrees of freedom, and replicability

  • Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafo, M. R. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14(5), 365-376. https://doi.org/10.1038/nrn3475
  • Colquhoun, D. (2014). An investigation of the false discovery rate and the misinterpretation of p-values. Royal Society Open Science, 1(140216). https://doi.org/10.1098/rsos.140216
  • Gelman, A., & Loken, E. (2013). The garden of forking paths: Why multiple comparisons can be a problem, even when there is no “fishing expedition” or “p-hacking” and the research hypothesis was posited ahead of time. Retrieved from http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf.
  • Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine, 2(8), 696-701. https://doi.org/10.1371/journal.pmed.0020124
  • Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359-1366. https://doi.org/10.1177/0956797611417632.

The issue of multiple comparisons and biased statistics in ERP analysis

General background on ANOVA and related issues

  • Kirk, R. E. (2013). Experimental Design (4th ed.). Thousand Oaks, CA: SAGE Publications.

Permutation and resampling statistics

General

  • Good, P. I. (2005). Permutation, Parametric, and Bootstrap Tests of Hypotheses (3rd ed.). New York, NY: Springer.
  • Manly, B. F. J. (1997). Randomization, Bootstrap, and Monte Carlo Methods in Biology (2nd ed.). London, UK: Chapman & Hall.

Permutation tests for ANOVA, factorial designs, and multiple regression

  • Anderson, M. J. (2001). Permutation tests for univariate or multivariate analysis of variance and regression. Canadian Journal of Fisheries and Aquatic Sciences, 58(3), 626-639. https://doi.org/10.1139/cjfas-58-3-626
  • Anderson, M. J., & Ter Braak, C. J. F. (2003). Permutation tests for multi-factorial analysis of variance. Journal of Statistical Computation and Simulation, 73(2), 85-113. https://doi.org/10.1080/00949650215733.
  • Freedman, D., & Lane, D. (1983). A nonstochastic interpretation of reported significance levels. Journal of Business & Economic Statistics, 1(4), 292-298. https://doi.org/10.2307/1391660
  • Still, A. W., & White, A. P. (1981). The approximate randomization test as an alternative to the F test in analysis of variance. British Journal of Mathematical and Statistical Psychology, 34(2), 243-252. https://doi.org/10.1111/j.2044-8317.1981.tb00634.x
  • Welch, W. J. (1990). Construction of permutation tests. Journal of the American Statistical Association, 85(411), 693-698. https://doi.org/10.2307/2290004
  • Wheldon, M. C., Anderson, M. J., & Johnson, B. W. (2007). Identifying treatment effects in multi-channel measurements in electroencephalographic studies: Multivariate permutation tests and multiple comparisons. Australian & New Zealand Journal of Statistics, 49(4), 397-413. https://doi.org/10.1111/j.1467-842X.2007.00491.x
  • Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014). Permutation inference for the general linear model. NeuroImage, 92, 381-397. https://doi.org/10.1016/j.neuroimage.2014.01.060

Multiple comparison correction procedures

The tmax/Fmax procedure

The cluster mass procedure

  • Bullmore, E. T., Suckling, J., Overmeyer, S., Rabe-Hesketh, S., Taylor, E., & Brammer, M. J. (1999). Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain. IEEE Transactions on Medical Imaging, 18(1), 32-42. https://doi.org/10.1109/42.750253.
  • Maris, E., & Oostenveld, R. (2007). Nonparametric statistical testing of EEG- and MEG-data. Journal of Neuroscience Methods, 164(1), 177-190. https://doi.org/10.1016/j.jneumeth.2007.03.024

False discovery rate

  • Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B: Methodological, 57(1), 289-300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
  • Benjamini, Y., Krieger, A. M., & Yekutieli, D. (2006). Adaptive linear step-up procedures that control the false discovery rate. Biometrika, 93(3), 491-507. https://doi.org/10.1093/biomet/93.3.491
  • Benjamini, Y., & Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29(4), 1165-1188. https://doi.org/10.1214/aos/1013699998

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