PCA compression

This function compresses data to n-dimensions using principal component analysis (singular value decomposition) as advocated in the following papers:

• Korhonen, Hernandez-Pavon, et al (2011) Removal of large muscle artifacts from transcranial magnetic stimulation-evoked EEG by independent component analysis. Med Biol Eng Compt, 49:397-407.

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

In these papers, PCA compression is run before EDM and PCA suppression respectively.

Example of output from PCA compression. Data were compressed from 60 to 30 dimensions.

EEGLAB user interface

1. Enter the number of dimensions to reduce the data by.

2. Turn on/off a plot which summarises the variance of each principal component before and after compression.

Scripts

Base function

EEG = tesa_pcacompress( EEG ); Default use.

EEG = tesa_pcacompress( EEG , 'key1', value1...); Custom inputs.

Pop function

EEG = pop_tesa_pcacompress( EEG ); Pop up window.

EEG = pop_tesa_pcacompress( EEG , 'key1', value1...); Custom inputs.

Required inputs

Input	Description	Example	Default
EEG	EEGLAB EEG structure	EEG	-

Optional inputs (key/value pairs)

Key	Input value	Description	Example	Default
'compVal'	integer	Integer describes the number of components to reduce the data to.	40	30
'plot'	'on' or 'off'	Turns on/off plot summarising the variance explained by principal components.	'off'	'on'

Outputs

Output	Description
EEG	EEGLAB EEG structure

Examples

EEG = pop_tesa_pcacompress( EEG, 'compVal', 40 ); Compress to top 40 dimensions.

EEG = pop_tesa_pcacompress( EEG, 'plot','off' ); Turns off summary plot.