Enhanced deflation method (EDM)

This function finds artifactual components automatically by using the enhanced deflation method (EDM) algorithm as advocated in the following paper:

• 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.

The function uses the same algorithmn as tesa_compselect to automatically detect components representing the TMS-evoked muscle artifact. Artifacts can be categorised as either TMS-evoked muscle activity or neural-other. Settings for this detection can be manually altered by the user.

EEGLAB user interface

1. Enter the number of components to consider. Leave blank for all. Note that the rank of the data will be reduced to this number.

2. Enter the number of components to plot. Leave blank for all.

3. Turn on or off automatic selection of components representing TMS-evoked muscle activity.

4. Set the threshold for detection of TMS-evoked muscle activity.

5. Set the target time window for detecting TMS-evoked muscle activity.

6. Turn on or off threshold feedback for TMS-evoked muscle activity. If on, values for each component will appear in the command window.

Scripts

Base function

EEG = tesa_edm( EEG ); Default use

EEG = tesa_edm( EEG, chanlocs, Nic, sf ); Custom inputs

EEG = tesa_edm( EEG, chanlocs, Nic, sf, 'key1', value1...); Settings for detecting comnponent representing TMS-evoked muscle activity.

Pop function

EEG = pop_tesa_edm( EEG ); Pop up window

EEG = pop_tesa_edm( EEG, chanlocs, Nic, sf ); Custom inputs

EEG = pop_tesa_edm( EEG, chanlocs, Nic, sf, 'key1', value1...); Settings for detecting comnponent representing TMS-evoked muscle activity.

Required inputs

Input	Description	Example	Default
EEG	EEGLAB EEG structure	EEG	-
chanlocs	Channel locations in structure identical to that used by EEGLAB (leave blank if chanlocs in EEG structure)	EEG. chanlocs	[ ]
Nic	Integer setting the number of independent components to look for. Note the rank of the data will be reduced to this number.	25	rank(EEG)-5 (to make sure the algorithm converges).
Sf	Sampling frequency in Hz. (Leave blank if Sf is in EEG structure)	1000	[ ]

Optional inputs (key/value pairs)

Key	Input value	Description	Example	Default
'comps'	integer	Integer describes the number of components to perform selection on (e.g. first 10 components). Leave empty for all components.	15	[ ]
'tmsMuscle'	'on' or 'off'	A string which turns on TMS-evoked muscle activity detection.	'off'	'on'
'tmsMuscleThresh'	integer	Integer determining the threshold for detecting components representing TMS-evoked muscle activity.	10	8
'tmsMuscleWin'	[start,end]	Vector describing the target window for TMS-evoked muscle activity (in ms).	[5,50]	[11,30]
'tmsMuscleFeedback'	'on' or 'off'	String turning on feedback of TMS-evoked muscle threshold value for each component in the command window. (Useful for determining a suitable threshold).	'on'	'off'

Outputs

Output	Description
EEG	EEGLAB EEG structure

Examples

EEG = pop_tesa_edm( EEG, [], 30, 1000); Only look for 30 components, sampling rate is 1000 Hz.

EEG = pop_tesa_edm( EEG, [], [], [], 'comps', 10, 'tmsMuscleThresh',10,'tmsMuscleWin',[11,50],'tmsMuscleFeedback','on'); Only plot the top 10 components, change the threshold for artefact detection to 10, change the window for comparions to 11-50 ms and return threshold values for each component in the command window.