ar_acf: Detect low autocorrelation of ICA components

In craddm/eegUtils: Utilities for Electroencephalographic (EEG) Analysis

Detect low autocorrelation of ICA components

Description

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)

• eeg_ICA: Autocorrelation checker for eeg_ICA objects

Author(s)

Matt Craddock [e-mail]

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

Examples

demo_sobi <- run_ICA(demo_epochs, pca = 10)
ar_acf(demo_sobi)

craddm/eegUtils documentation built on March 24, 2022, 9:17 a.m.