PCA suppression

This function suppresses data by the top PCA n-dimensions. Note that PCA is generated from average data, but subtracted from single trial data. For further details, see the following paper:

• Hernandez-Pavon et al (2012) Uncovering neural independent components from highly artifactual TMS-evoked EEG data. J Neurosci Meth, 209:144-57.

After the function is run, the user needs to decide how many principal components to remove. A summary figure showing the impact of removing between 1 and 5 of the top components on the EEG data is provided to assist with the decision.

Example of the summary figure following PCA suppression. The top figure shows the impact of removing between 1-5 principal components on the data. Once the user has decided how many PCs to remove, the number (between 0-5) is entered in to second window.

EEGLAB user interface

1. Enter the time range considered by the principal component analysis. This should be the time range in which the TMS-evoked muscle artifact is expected. Times are in ms.

Scripts

Base function

EEG = tesa_pcasuppress( EEG, timeWin ); Default use.

Pop function

EEG = pop_tesa_pcasuppress( EEG ); Pop up window.

EEG = pop_tesa_pcasuppress( EEG, timeWin ); Custom inputs.

Required inputs

Input	Description	Example	Default
EEG	EEGLAB EEG structure	EEG	-
timeWin	Vector setting time window for data suppression in ms. [start, end]	[11,50]	-

Outputs

Output	Description
EEG	EEGLAB EEG structure

Examples

EEG = pop_tesa_pcasuppress( EEG, [11, 50] );