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.