Dear all,
It seems my data are suffering from alpha distortion. It is possible to reduct it from some data with the help of ICA but for half of the participant ICA does not work that well.
So for now I tried to reject more components than usual (15-18 out of 64 ICs) and run the ICA again if it can catch the alpha. But it doesn't work.
In a nutshell, I wonder what are the best ways to reduce the alpha disturtion from data
Note: I also heart that PARAFAC seems to work better then ICA for reduction of Alpha but I cannot find a way to apply PARAFAC with EEGLAB GUI
Best,
Behcet
HI,
See the section "Studying and removing ICA components". You must first check each Alpha Power ICA component. Then, you can delete, ICA has no alpha power and only keep ICA with alpha power. I hope this link helps you.
https://sccn.ucsd.edu/wiki/Chapter_09:_Decomposing_Data_Using_ICA
I do not think it is a good idea to purposely delete ICs from your data that you believe are present due to brain activity. In most cases it is not a defensible assumption that this activity is 100% not relevant to what you are measuring, especially if it is consistent enough to occur during an ERP average. Also, it is not a good idea to re-run an ICA after you have removed ICs because you have reduced the number of dimensions in the data. In this case, you would have to run a PCA and enter the number of components that were left in the original dataset to obtain a valid decomposition.
I am not sure of your experimental design, but one of the best ways to reduce the amount of alpha activity in an ERP average is to use a baseline correction that is a multiple of 100ms. Because the dominant frequency of alpha oscillations is 10Hz, this value will ensure that there are an equal number of alpha oscillations in the baseline which will normally remove most of the alpha waves. If this does not work, you may be able to isolate the alpha waves in an ERP average by using Joe Dien's PCA toolkit to run a temporo-spatial PCA, which you can read your final EEGLAB/ERPLAB files into.
One solution would be to use the intertrial interval as the training data for the ICA detection. This way you make sure no brain activity related to the task exist in that data and then remove the ICA components from original data by exporting the ICA to original data. please see: Article The Right Temporoparietal Junction Supports Speech Tracking ...
hope it helps.
Rouhi
Hello Behçet,
If you are estimating latencies and amplitudes of traditional event-related potentials (ERPs), then a targeted filtering approach is a more direct method of removing alpha (and other frequency-band) influences. I typically use a filter band pass of .1 to 10 Hz (forward and backward Butterworth to remove phase distortions) for ERPs, although a smaller low pass may be needed (.1 to 5 Hz ?) to remove large amplitude alpha activity.