I am looking for an algorithm (possibly with MATLAB code) that allows for blind source separation of correlated nonlinear mixtures in EEG data as an alternative approach to independent component analyses (ICA).
With this I would like to dissociate potentially nonlinear mixtures of correlated sources reflecting visual, vestibular and proprioceptive (neck muscle) activity during active head rotations.
I appreciate any pointer to existing toolboxes or algorithms.
This is an interesting question. But I doubt the existence of such algorithms (with real effectiveness) . In fact, I strongly believe solving this issue (removing artifacts from active head rotations) calls for breakthrough in sensor design. But I would like to see others' answers.
Zhilin
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