[Eeglablist] Calculate ICA on short 'cleaned' epoch, then apply to long epoch
Xiaoming Du
XDu at mprc.umaryland.edu
Tue Jun 7 12:52:37 PDT 2016
Hi all,
I have a rookie question about using ICA to remove specific artifacts. Please correct me if I was wrong.
(1) I should feed ICA with 'cleaned' epochs (less non-stereotypic noise) and with enough data points (> k*N^2).
(2), I can calculate ICA on short epochs, then apply the ICA (weights and sphere matrices) to long epochs from the same dateset.
(reference link: http://sccn.ucsd.edu/wiki/Chapter_09:_Decomposing_Data_Using_ICA#Studying_and_removing_ICA_components)
For my data set, I want to remove the known artifacts that occurs right after the events (within 100 ms). Also, there are only 13 channels (including 2 eye-channels) for each subject.
My goal is to remove this special artifacts using ICA on long epochs. However, because I already know the artifacts occurs shortly after events, I want ICA focus on this short window to identify the artifact-components. I want to minimize the effects of other artifacts or noise on ICA.
My current thoughts are: 1, cut continuous file into long epochs (4 seconds).
2, remove epochs with non-stereotypic noise.
3, 1-hz high-pass filter the remaining long epochs, then cut short epochs (-30 to 100 ms). In this way, the short epochs mainly contains the artifacts I want to remove.
4, apply ICA on the 0.13-second short epochs. The sampling rate of our data is 1000 Hz. There are more than 50 trials. Therefore, The data point for ICA are around 1000*0.13*50 = 6500. This is larger than 5070 (30*13^2), so I should have enough data for ICA on those short epochs.
5, identify the artifact-components from step 4.
6, apply the ICA weights and sphere to long epochs (before 1-hz highpass filter), then reject artifact-components identified from step 5.
7, for now, I should have long epochs that are artifact-corrected and not filtered.
Please let me know if those steps are reasonable. Any suggestions or comments are appreciated!
Thanks.
Xiaoming
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