[Eeglablist] Data analysis with few channels

[Emmanuelle Renauld]

Hi all,


I have been thinking a lot about the best way to analyse my data, considering that I have very few channels (8. Thus 7 after re-referencing).

So far, I do:
1. Re-referencing.
2. High-pass
3. Low-pass
4. ICA decomposition

However: with only 7 channels, I often had very bad result at ICA. Looking at the signals, I saw many bugs happening on nearly all electrodes at the same time. If I cut the signal around that bug (ex, remove some data from EEG.data and EEG.times), the spectrum is usually already cleaner, and the ICA decomposition also works better (for instance, alpha frequencies are then well separated from the eye blinks and eye movements components).

So I started thinking about "epoching" my data, but I don't have events to do that. Something like cutting my data into windows of maybe 1 second, removing windows where the signal is too big, and then computing spectrums and ICA. I did it manually, cutting usually 2-3 sections in the data, the worst parts, of usually around 10 seconds each. I was supervising the results, and it worked, but if I start doing it automatically, I fear that the number of windows rejected increase. What do you think would be the effect of computing ICA or spectrums on such annexed data? Could it, for instance, create false frequencies?

I have two types of data: at rest, or doing a DDT task.

Thank you very much!


Emmanuelle