How to train a neural network, working on eeg, to deal with missing channels
2 views (last 30 days)
Show older comments
I am building a data set which will be used to train a neural network to recognize specific signals in 20 channels of eeg (i.e. input is 20xN values). Some of the data used to train this network (as well as expected future input) will only contain 18 or 19 of these channels. Is there a way to train this network to deal with these unknowns????
(I have seen articles on averaging values and the like. I'm looking for something a bit more sophisticated?)
0 Comments
Answers (1)
Greg Heath
on 26 Oct 2018
You may have to create a series of increasingly sophisticated interpolation techniques.
1. Start with a baseline of zeros for missing data 2. Next try linear interpolation 3. Typically, that's it for me. However, you could move on to quadratic interpolation or fourier series.
Hope this helps.
*Thank you for formally accepting my answer*
Greg
0 Comments
See Also
Categories
Find more on EEG/MEG/ECoG in Help Center and File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!