[Eeglablist] A method to do group analysis using SIFT.

[Nikola Tesla]

Hi, everyone
 
   SIFT doesn’t have Group Analysis module in current version. But I want to use this toolbox do granger-causal connectivity analysis of channel data. To achieve this goal, I have thought out this method. But I don’t know whether it make sense or not.
 
   I will use the experiment in the SIFT manual illustrate my method. Assuming I have got the data of 10 subjects in two conditions (RespWrong and RespCorrect). So there are 20 datasets in total. Independent component analysis has been conducted on all of the 20 datasets. In each dataset, the number of channels and independent components both are 152(nbchan = 152 ), and the number of epochs is 123. In every epoch, there are 1024 data points(ie. pnts = 1024).
 
   Then I collected the EEG data of the eleventh subject in the two conditions (RespWrong and RespCorrect). And these two dataset are called ‘datasetwrong’ and ‘datasetcorrect’. Independent component analysis has been conducted on all of the 20 datasets. In each dataset, the number of channels and independent components both are 152(nbchan = 152 ), and the number of epochs is 1030. In every epoch, there are 1024 data points(ie. pnts = 1024).
 
   Now I want to do granger-causal connectivity analysis of the RespWrong condition. The No. of channels for analysis are 11, 12, and 13. For the first ten subjects, I get 1230(123*10) epochs at each channel. Firstly, I get 1000 epochs which are free from artifacts from all the 1230 epochs in channel 1. This procedure will be repeated for channel 2 and channel 3. Then using these data, I get 3 1024*1000 matrices. They are called a, b, and c. And I assume these three matrices represent the data from all of the first ten subjects. Secondly, datasewrong is loaded in MATLAB, and 30 epochs in this dataset are deleted, which means the number of the remaining epochs is 1000. Thirdly, in the command line, I type EEG.data(1,:,: ) = a ; EEG.data(2,:,: ) = b; EEG.data(3,:,: ) = c.  EEG.data is a 152*1024*1000 matrix. Fourthly,  in the command line, I type EEG.data = EEG.icaact; EEG.icawinv = EEG.icaweights = ones(152,152). Lastly, I select the eleventh, twelfth, and thirteenth independent components and do granger-causal connectivity analysis according to the Data processing pipeline illustrated in the SIFT manual.
 
   I’m asking my method is right or not? Can it be considered as an alternative for group analysis?
 
   Can I do bootstrap sample and phase randomization with the current version of SIFT?
 
  Anyone who konws the answer please tell me. Thanks!
 
 
 
Sincerely,
 
Hui-bin Jia
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