A fully automatic algorithm, implemented in MATLAB, for finding Eye-Related ICA components based on their scalpmaps and spectrum signatures (new). It has a performance comparable to CORRMAP while not requiring any user intervention.
Here is a sample of eye-related ICA scalpmaps used in EyeCatch:
Measure Projection Toolbox (MPT) includes EyeCatch software (as pr.eyeCatch class), if you have not installed MPT you can download EyeCatch stand-alone from this repository.
If you used EyeCatch, please include a reference to EyeCatch paper (below) in your publication :
Note: if you have already installed Measure Projection software, please use pr.eyeCatch instead of eyeCatch in the examples below.
Example 1: Finding eye ICs in the EEG structure ().
>> eyeDetector = eyeCatch; % create an object from the class. Once you made an object it can
% be used for multiple detections (much faster than creating an
% object each time).then
>> [eyeIC similarity scalpmapObj] = eyeDetector.detectFromEEG(EEG); % detect eye ICs
>> eyeIC % display the IC numbers for eye ICs.
>> scalpmapObj.plot(eyeIC) % plot eye ICsExample 2: (application on a study)
>> eyeDetector = eyeCatch; % create an object from the class. Once you made an object it can
% be used for multiple detections (much faster than creating an
% object each time).
% read data from a loaded study
>> [isEye similarity scalpmapObj] = eyeDetector.detectFromStudy(STUDY, ALLEEG);
>> find(isEye) % display the IC numbers for eye ICs (since isEye is a logical array). The order of ICs is same order as in STUDY.cluster(1).comps .
>> scalpmapObj.plot(isEye) % plot eye ICsCreated by Nima Bigdely-Shamlo, PhD.