INFORMATION THEORETIC FEATURE PROJECTION FOR SINGLE-TRIAL BRAIN-COMPUTER INTERFACES.

Current approaches on optimal spatio-spectral feature extraction for single-trial BCIs exploit mutual information based feature ranking and selection algorithms. In order to overcome potential confounders underlying feature selection by information theoretic criteria, we propose a non-parametric feature projection framework for dimensionality reduction that utilizes mutual information based stochastic gradient descent. We demonstrate the feasibility of the protocol based on analyses of EEG data collected during execution of open and close palm hand gestures. We further discuss the approach in terms of potential insights in the context of neurophysiologically driven prosthetic hand control.