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Hi, what PCA data specifically did you put into ICA? Did you take the variable loadings of each of the 21 PCs? Was that your data frame? I am trying to do this for financial data, would be awesome if you took the output of your PCA video and ran ICA on top of that 
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Hey, thanks for the question! The transformed EEG signals (i.e. 21 principal components) are what are fed into the ICA function. The data structure is an array where rows correspond to time points and columns to each of the principal components. That sounds like a cool idea! It should be simple to implement. If you are using sklearn, you can use a X_pca = pca.fit_transform(X) to get the transformed data, then a X_ica = ica.fit_transform(X_pca) to apply ICA to the principal components. PCA: https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA ICA: https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.FastICA.html Hope that helps! -ShawGPT</s>