  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. Methods: We present a maximum
mutual information linear transformation (MMI-LinT), and a nonlinear
transformation (MMI-NonLinT) framework derived by a general definition of the
feature transformation learning problem. Empirical assessments are performed
based on electroencephalographic (EEG) data recorded during a four class motor
imagery brain-computer interface (BCI) task. Exploiting state-of-the-art
methods for initial feature vector construction, we compare the proposed
approaches with conventional feature selection based dimensionality reduction
techniques which are widely used in brain interfaces. Furthermore, for the
multi-class problem, we present and exploit a hierarchical graphical model
based BCI decoding system. Results: Both binary and multi-class decoding
analyses demonstrate significantly better performances with the proposed
methods. Conclusion: Information theoretic feature transformations are capable
of tackling potential confounders of conventional approaches in various
settings. Significance: We argue that this concept provides significant
insights to extend the focus on feature selection heuristics to a broader
definition of feature transformation learning in brain interfaces.
