Using ICA on EEG signals for feature extraction I am attempting to use ICA (FastICA via scikit-learn) on EEG signals from seven electrodes per subject for feature extraction and identity classification – that is to extract signal which is related to a specific cortical area without noise and feed it to e.g. MLP for classification.

As I currently understand it, I should be able to get at most seven individual components which I seem to be achieving, but I do not understand how to actually choose the one related to a specific area nor how to verify the results. How do I know I am not extracting noise (they are all very similar)?

The data have been pre-processed with a Butterworth bandpass filter (4 – 40 Hz). The mean error for classification of six subjects via MLP is currently around 12% but similar studies achieved far better results.

See for example:


Selecting Relevant EEG Signal Locations for Personal Identiﬁcation using ica and neural network
Personal Identification by EEG Using ICA and Neural Network


Excuse the lameness with this one, please – the research papers and introductions to ICA I have read are not brilliant at explaining and use examples involving ideal components.

Anyone?
How do I know I am not extracting noise (they are all very similar)?


You should first find or record a well understood set of signals with which you can test your feature extraction code to verify your setup is correct and that the extracted components are as expected.

Then you'll have higher confidence that you're not extracting noise due to errors in your code and you can focus on tuning your algorithms or reproducing other's results.