I'm working on a project predicting seizures events using 10 minute EEG data recordings. This involves identifying the preictal (pre-seizure) phase from the interictal (normal/between seizure) phase, so binary classification.
There are 16 electrodes, therefore 16 channels in my data. I have read that calculating the cross-channel power density spectrum is a popular method for this kind of problem, but this would result in a high number of dimensions so I want to know if it is okay to use dimensionality reduction on cross-channel derived features and if so, which technique would be best.
I have tried PCA, but I read that features with maximised variance is not a good method for classifying preictal and interictal recordings. LDA would have been nice to use as this is supervised learning, but I only have two classes so I can't use this.
I'm interested if ICA would work in this instance, or would it just end up identifying the singular channel power densities? Or possibly random forests?