Detect low autocorrelation of ICA components

Low autocorrelation can be a sign of a poor quality channel or component. Often these are noisy, poor contact, or heavily contaminated with muscle noise. Low autocorrelation at a lag of 20ms is often associated with muscle noise.

Usage

ar_acf(data, ...)

# S3 method for class 'eeg_ICA'
ar_acf(data, ms = 20, plot = TRUE, verbose = TRUE, threshold = NULL, ...)

Arguments

data: eeg_ICA object
...: additional parameters
ms: Time lag to check ACF, in milliseconds. Defaults to 20 ms.
plot: Produce plot showing ACF and threshold for all EEG components.
verbose: Print informative messages. Defaults to TRUE.
threshold: Specify a threshold for low ACF. NULL estimates the threshold automatically.

Value

A character vector of component names that break the threshold.

Methods (by class)

ar_acf(eeg_ICA): Autocorrelation checker for eeg_ICA objects

References

Chaumon, M., Bishop, D.V., Busch, N.A. (2015). A practical guide to the selection of independent components of the electroencephalogram for artifact correction. J Neurosci Methods. Jul 30;250:47-63. doi: 10.1016/j.jneumeth.2015.02.025

Author

Matt Craddock matt@mattcraddock.com

Examples

demo_sobi <- run_ICA(demo_epochs, pca = 10)
#> Reducing data to 10 dimensions using PCA.
#> Running SOBI ICA.
ar_acf(demo_sobi)
#> Estimating autocorrelation at 20ms lag.
#> Estimated ACF threshold: -0.16

#> Subthreshold components:  
#> character(0)