Information Theoretic Feature Transformation Learning for Brain Interfaces.
Journal:
IEEE transactions on bio-medical engineering
Published Date:
Mar 28, 2019
Abstract
OBJECTIVE: A variety of pattern analysis techniques for model training in brain interfaces exploit neural feature dimensionality reduction based on feature ranking and selection heuristics. In the light of broad evidence demonstrating the potential sub-optimality of ranking-based feature selection by any criterion, we propose to extend this focus with an information theoretic learning-driven feature transformation concept.