Optimizing a Weighted Moderate Deviation for Motor Imagery Brain Computer Interfaces

Abstract

Brain-Computer Interfaces based on the analysis of ElectroEncephaloGraphy (EEG) are composed of several elements to process and classify brain input signals. A relevant phase of these systems is the decision making module, in which often the outputs from different classifiers are fused into a single one. In this work, the use of weighted-moderate deviation based functions is proposed to improve the Enhanced-Multimodal Fusion BCI Framework (EMF) decision making phase. Moderate Deviation-based aggregation functions (MDs) allow us to choose the best value to aggregate a vector of points involving a moderate deviation function. Using a weighted MD, the relative importance of each dimension in the multi-dimensional aggregated data set can also be taken into account. By applying these functions in the EMF, each one of the different brain signals can be weighted according to their importance. Moreover, using automatic differentiation, it is possible to optimize them for the present problem.